Association Between Urbanicity–Cooking Fuel Type and Acute Respiratory Infection Symptoms Among Children Under Two Years across Ten East African countries.

Association Between Urbanicity–Cooking Fuel Type and Acute Respiratory Infection Symptoms Among Children Under Two Years across Ten East African countries. | 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 comment Association Between Urbanicity–Cooking Fuel Type and Acute Respiratory Infection Symptoms Among Children Under Two Years across Ten East African countries. Abebayehu N. Yilma, Hannah E. Sauve, Kirstin P. West, Kedir Teji Roba, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8920900/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Introduction: Limited evidence exists on how urbanicity in LMICs and fuel type affect infant ARI risk. This study examined the prevalence of cough and dyspnea across ten East African countries and the associations of these symptoms with fuel type and residence among children aged under two years. Methods: Pooled Demographic and Health Survey data (2015–2023) from mother–child pairs across ten East African countries were analyzed. Outcomes included cough and dyspnea in children reported by the mother within the past 2 weeks. The exposure was a four-category of cooking fuel type and residence (urban-clean vs rural-clean, urban-unclean, rural-unclean). Multivariable logistic regression was adjusted for child, maternal, and household characteristics. Results: There were 38,849 infants. Cough prevalence ranged from 13 (Mozambique) to 44% (Uganda), and dyspnea from 3% (Tanzania) to 21% (Uganda). Most households (96%) used unclean fuels, and 79% were rural. Compared with urban–clean households, the adjusted odds of cough were higher in urban–unclean (aOR=1.36; 95% CI:1.06–1.74) and rural–unclean households (aOR=1.40; 95% CI:1.09–1.81). Dyspnea odds were higher in urban–unclean (aOR=2.21; 95% CI:1.46–3.74) and in rural–unclean households (aOR=3.08; 95% CI:1.89–5.01). Children aged 12–23 months had higher odds of cough (aOR=1.16; 95% CI: 1.09-1.22) than those 0–11 months. Conclusions: Children under 2 in households using unclean fuel, whether rural or urban, experienced more cough and dyspnea than those using clean fuel. Promoting clean fuel and early health care could reduce ARI symptoms in LMICs. Acute respiratory infection-related symptom cough dyspnea children under 2 years old urban and rural unclean cooking fuel East Africa Demographic Health Survey Figures Figure 1 Introduction Acute respiratory infections (ARIs) remain one of the leading causes of morbidity and mortality among children under five years of age, with even younger children being particularly vulnerable ( 1 , 2 ). ARIs encompass infections of both upper and lower respiratory tract ( 3 ). Globally, an estimated 1.3 million children under five die from ARIs each year ( 4 ), with the overwhelming majority of deaths occurring in low-and middle-income countries (LMICs) ( 5 ). In East Africa,, ARIs contribute significantly to childhood morbidity and mortality ( 6 ), exacerbating existing socioeconomic inequities ( 7 ). Exposure to household air pollution from the combustion of unclean cooking fuels is a major and potentially modifiable risk factor for ARIs in children ( 8 , 9 ). According to the World Health Organization (WHO), unclean cooking fuels are unprocessed coal, kerosene, and biomass fuels ( 10 ). Common sources of cooking fuel include unprocessed coal, kerosene, and a range of biomass fuels such as wood and agricultural byproducts ( 11 ). Burning these fuels in poorly ventilated kitchens or inefficient stoves releases high concentrations of respirable particulate matter (PM 2.5 ) and toxic gases. Kitchens using biomass fuels in LMICs commonly reach 24-hour PM 2.5 levels of 200–3,000 µg/m³ ( 12 ), far exceeding the WHO guidelines values being no more than 5 µg/m³ and a 24-hour mean of 15 µg/m³ ( 13 ). PM 2.5 penetrates deep into the respiratory tract, triggering inflammation and ARIs ( 14 , 15 ). Children are particularly susceptible due to immature lungs, higher ventilation rates relative to body size, proximity to cooking areas, and increased exposure to infectious agents ( 16 ). More than half of the world’s unelectrified population lives in Eastern and Southern Africa, and nearly a quarter of those globally lacking access to clean cooking fuel also reside in this region. ( 17 ). As a result, reliance on solid biomass fuels remains pervasive. Gendered household roles further increase exposure: women and young children—often carried during cooking—experience disproportionately high indoor pollution levels ( 18 ). Previous studies examining household energy use and ARIs have primarily focused on children under five years of age and fuel type alone. Country-level analyses using Demographic and Health Survey (DHS) or government-administered survey data consistently show increases ARI-related symptom risk associated with unclean fuel use ( 5 , 9 , 19 ). Additional determinants include younger age, teenage motherhood, low maternal education, poverty, limited access to media, low birth weight, absence of breastfeeding, deficient water and sanitation access, and poor household conditions ( 20 , 21 ). Despite the high burden of ARIs and household air pollution in East Africa, few regional analyses have examined how urbanicity modifies the association between cooking fuel type and ARI-related symptoms risk—especially among children under two or one year of age ( 21 ). To address these evidence gaps, we aimed to examine the associations between urbanicity, household cooking fuel type, and symptoms among children under two years old across ten East African countries. Using pooled nationally representative DHS data, the analysis aims to generate regional evidence while accounting for key confounders, including socio-demographic, household, and environmental factors. Materials and Methods Study setting and data source East Africa comprises 19 countries according to the United Nations classification. This study included ten East African countries with Demographic and Health Survey (DHS) data collected between 2015 and 2023 (Burundi, Comoros, Djibouti, Ethiopia, Eritrea, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Somaliland, Tanzania, Uganda, Zambia, and Zimbabwe); countries without DHS surveys (Djibouti, Somalia, Somaliland, the Seychelles, Mauritius, and the Réunion) or with surveys conducted before 2015 (Eritrea, Comoros, and Zimbabwe) were excluded. DHS data were obtained from the official DHS Program database following approval. Appendix Table 1 shows the year of the survey in the included countries. DHS surveys use nationally representative, stratified, multistage sampling and collect data from women aged 15–49 years ( 22 ). The cross-sectional data from the Children’s Recode, drawn from the Women’s Questionnaire ( 23 ). The analytic sample included 38,849 women with children under two years old. Ethic statement The DHS Program obtains ethical approval from the Institutional Review Board and national ethics committees. All DHS surveys follow ethical protocols like informed consent, confidentiality, and anonymity. Since this analysis used de-identified secondary data, no further ethical approval was needed. Access was granted via the DHS data request process. All analyses adhered to DHS Terms of Use and research guidelines. Outcome variables The outcome variables in this study were ARI-related symptoms, including cough and dyspnea (short rapid breath) in the last 2 weeks. The symptoms were reported by the child's mother. Both variables were analyzed separately and coded as binary outcomes. The cough variable was derived from the survey question asking whether the child had had a cough in the last 2 weeks (h31). Cough was recoded into a dichotomous variable indicating whether the child had experienced cough (“Yes” encoded as 1) or not (“No” encoded as 0). The variable dyspnea (short rapid breath) originates from the question in the survey inquiring whether the child had suffered from rapid breathing in the last 2 weeks (h31b), which was left as the original yes ( 1 ) or no (0) response. The outcome variables, cough and dyspnea, were analyzed separately. Exposure variables Cooking fuel was categorized as clean or unclean based on WHO definitions ( 10 ). Urbanicity was classified as urban or rural. These variables were combined to create four exposure categories: urban-clean, urban-unclean, rural-clean, and rural-unclean (24). Type of cooking fuel: The exposure variables in this study were derived from the variables' type of cooking fuel' and 'type of residence' The cooking fuel type was recoded as clean (electric, biogas, natural gas, and liquefied petroleum gas) and unclean (coal/lignite, kerosene, charcoal, wood, straw/shrubs/grass, agricultural crop, and animal dung). Residences: Type of residence or “urbanicity” was recoded into urban and rural, while responses indicating abroad dropped. These two variables were used to create the exposure variable for this study: the type of cooking fuel used. To create the exposure variable for this study, four mutually exclusive categories were defined: urban-clean, urban-unclean, rural-clean, and rural-unclean. Covariates Covariates were chosen based on existing literature indicating they could influence the independent and dependent variables and potentially confound the results. When examining the relationship between ARI-related symptoms and urbanicity, other potential confounding variables from the existing literature were included in the analyses ( 21 ). These variables fell into three categories: child-, mother-, and household characteristics (summarized in Appendix 1: Table 2 ). Statistical analysis The analysis was conducted using R (version 4.0.5; R Core Team, 2021). We considered statistical significance at p < 0.05. The svydesign() function was used to ensure that survey design information was incorporated into the analyses. Geospatial analyses and visualization were conducted in R and ArcGIS Online. R was used to calculate the prevalence of ARI-related symptoms and clean cooking fuel by country. The prevalence of ARI-related symptoms and clean cooking fuel use was calculated by dividing the number of cases by the total number of responses, then multiplying by 10,000. Missing responses regarding cough and dyspnea, and “I don’t know” responses made up less than 10% of total responses and were therefore removed. Missing responses, “abroad” responses for the type of residence variable, and “No food cooked in house”, “Other”, and “Not a de jure resident” responses to the kind of cooking fuel were removed. Missing responses for covariates were dropped if they accounted for less than 10% of the data. The variable ‘water source’, missing over 10% of responses, was imputed using the K-nearest neighbors’ approach (R function kNN(); kNN imputation estimates missing values based on the most similar observations in the dataset, making it suitable for categorical variables (25). Similarity between observations was determined using an appropriate distance metric, and the most frequent category among the k nearest neighbors was assigned. The number of neighbors was set to k = 6 to balance local similarity and robustness of the estimates.Variable responses such as “don’t know”, “abroad”, “other”, “not a de jure resident” were removed for the analyses. Bivariate analysis was conducted using a chi-square test of independence to examine associations between each outcome variable and each exposure and covariate. To further explore the relationships among cough, dyspnea, and cooking-fuel type, logistic regression models were fitted for each outcome variable. The main model, stratified by cough and dyspnea, was adjusted for all variables with p-values < 0.2 in the bivariate analysis. Previous studies found that liberal inclusion criteria, such as p < 0.2, help avoid missing important predictors (26). To ensure survey weights were accounted for, the svyglm() function with a quasibinomial family was used for these regressions. Weighted odds ratios (ORs) and 95% confidence intervals (CIs) were reported. The additional models included a crude model (Model 1), a model adjusted only for the child’s characteristics (Model 2), a model adjusted only for the mother’s characteristics (Model 3), and a model adjusted only for household characteristics (Model 4). Results Basic characteristics of the study population Table 1 presents the basic characteristics of 38,849 mother–child pairs across 10 East African countries. Most households (96%) used unclean cooking fuels, and 79% of these were in rural areas. Only 4% of households reported using clean fuels, predominantly in urban settings. Slightly more than half of the children (52%) were aged 0–11 months, and an equal distribution by sex (50% males). More than three-quarters of mothers delivered in health facilities (77%), and a large proportion (73%) did not receive postnatal care. The number of missing variables is shown in Supplementary Table 1. Table 1 Basic child (under two years old), maternal, and household characteristics stratified by symptom, such as cough and dyspnea, both or neither of symptoms across ten East African countries, 2015–2023 Demographic and Health Survey (DHS) data. Variables Overall Cough Dyspnea Both Neither N = 38,849 N = 7,237 N = 796 N = 3,902 N = 26,914 Cooking fuel and residence Clean-urban 1,309 (3.4%) 254 (3.5%) 5 (0.6%) 42 (1.1%) 1,008 (3.7%) Clean-rural 309 (0.8%) 47 (0.6%) 2 (0.2%) 18 (0.5%) 243 (0.9%) Unclean-urban 6,580 (17%) 1,362 (19%) 91 (11%) 488 (13%) 4,638 (17%) Unclean-rural 30,651 (79%) 5,574 (77%) 699 (88%) 3,354 (86%) 21,025 (78%) Age of child 0–11 months 20,228 (52%) 3,490 (48%) 421 (53%) 1,973 (51%) 14,344 (53%) 12–23 months 18,621 (48%) 3,746 (52%) 376 (47%) 1,929 (49%) 12,570 (47%) Sex of Child Male 19,517 (50%) 3,627 (50%) 404 (51%) 2,045 (52%) 13,440 (50%) Female 19,332 (50%) 3,610 (50%) 392 (49%) 1,857 (48%) 13,474 (50%) Child’s weight at birth Underweight 4,117 (11%) 818 (11%) 91 (11%) 454 (12%) 2,754 (10%) Normal 34,732 (89%) 6,419 (89%) 706 (89%) 3,448 (88%) 24,160 (90%) Perceived size of child at birth Small 6,653 (17%) 1,354 (19%) 168 (21%) 817 (21%) 4,314 (16%) Average 20,888 (54%) 3,706 (51%) 348 (44%) 1,822 (47%) 15,012 (56%) Large 11,308 (29%) 2,176 (30%) 280 (35%) 1,264 (32%) 7,588 (28%) Birth order of child 1 9,216 (24%) 1,833 (25%) 179 (23%) 942 (24%) 6,262 (23%) 2 to 4 19,164 (49%) 3,638 (50%) 387 (49%) 1,795 (46%) 13,345 (50%) 5 and above 10,469 (27%) 1,767 (24%) 230 (29%) 1,165 (30%) 7,307 (27%) Mother's education level No education 8,666 (22%) 1,332 (18%) 179 (22%) 961 (25%) 6,194 (23%) Primary 19,730 (51%) 3,785 (52%) 441 (55%) 2,118 (54%) 13,386 (50%) Secondary 8,856 (23%) 1,754 (24%) 157 (20%) 730 (19%) 6,215 (23%) Higher 1,597 (4.1%) 366 (5.1%) 20 (2.5%) 92 (2.4%) 1,119 (4.2%) Mother's employment No 15,117 (39%) 2,296 (32%) 239 (30%) 1,178 (30%) 11,404 (42%) Yes 23,732 (61%) 4,941 (68%) 557 (70%) 2,724 (70%) 15,510 (58%) Mother’s age at first birth < 20 years 22,703 (58%) 4,099 (57%) 473 (59%) 2,317 (59%) 15,814 (59%) 20–49 years 16,146 (42%) 3,138 (43%) 323 (41%) 1,585 (41%) 11,100 (41%) Mother's Marital Status Never married, widowed, separated 5,967 (15%) 1,215 (17%) 97 (12%) 550 (14%) 4,105 (15%) Married and living with partner 32,882 (85%) 6,022 (83%) 699 (88%) 3,352 (86%) 22,809 (85%) Antenatal care visits Yes 36,161 (93%) 6,950 (96%) 765 (96%) 3,613 (93%) 24,833 (92%) No 2,688 (6.9%) 286 (4.0%) 32 (4.0%) 289 (7.4%) 2,081 (7.7%) Postnatal care visit No 28,246 (73%) 5,168 (71%) 589 (74%) 2,812 (72%) 19,676 (73%) Yes 10,603 (27%) 2,068 (29%) 207 (26%) 1,090 (28%) 7,238 (27%) Place of child’s delivery Home 9,063 (23%) 1,339 (18%) 176 (22%) 962 (25%) 6,586 (24%) Health Facility 29,786 (77%) 5,898 (82%) 620 (78%) 2,940 (75%) 20,328 (76%) 55 years 250 (0.6%) 44 (0.6%) 6 (0.8%) 20 (0.5%) 179 (0.7%) Frequency of smoking Doesn't smoke 38,600 (99%) 7,193 (99%) 790 (99%) 3,882 (99%) 26,735 (99%) Every day 79 (0.2%) 16 (0.2%) 3 (0.3%) 6 (0.2%) 54 (0.2%) Some days 171 (0.4%) 28 (0.4%) 4 (0.4%) 14 (0.4%) 126 (0.5%) Age of household head 55 years 3,611 (9.3%) 675 (9.3%) 52 (6.5%) 359 (9.2%) 2,526 (9.4%) Sex of household head Male 30,737 (79%) 5,649 (78%) 628 (79%) 3,105 (80%) 21,355 (79%) Female 8,112 (21%) 1,588 (22%) 168 (21%) 797 (20%) 5,559 (21%) Type of residence Urban 7,889 (20%) 1,616 (22%) 96 (12%) 530 (14%) 5,647 (21%) Rural 30,960 (80%) 5,621 (78%) 700 (88%) 3,372 (86%) 21,268 (79%) Household wealth status Poor 17,274 (44%) 3,052 (42%) 358 (45%) 1,877 (48%) 11,986 (45%) Middle 7,585 (20%) 1,432 (20%) 172 (22%) 756 (19%) 5,225 (19%) Rich 13,990 (36%) 2,752 (38%) 266 (33%) 1,269 (33%) 9,703 (36%) Main source of drinking water Improved 30,434 (78%) 5,949 (82%) 655 (82%) 3,106 (80%) 20,723 (77%) Unimproved 8,415 (22%) 1,287 (18%) 141 (18%) 796 (20%) 6,191 (23%) Type of toilet facility Improved 18,879 (49%) 3,704 (51%) 376 (47%) 1,740 (45%) 13,059 (49%) Unimproved 19,970 (51%) 3,532 (49%) 421 (53%) 2,162 (55%) 13,855 (51%) Electricity No 29,163 (75%) 5,334 (74%) 654 (82%) 3,236 (83%) 19,939 (74%) Yes 9,686 (25%) 1,903 (26%) 142 (18%) 666 (17%) 6,975 (26%) Access to media Yes 18,312 (47%) 3,618 (50%) 344 (43%) 1,658 (42%) 12,692 (47%) No 20,537 (53%) 3,619 (50%) 452 (57%) 2,244 (58%) 14,222 (53%) Notes: ∗A difference of one observation between the maternal smoking variables (N = 38,850) and the total analytic sample (N = 38,849) reflects a case with complete maternal smoking data but missing information in another covariate before imputation or analytic dataset construction. Prevalence of cough and dyspnea in infants The prevalence of cough in children under two years old ranged from 1,267 per 10,000 children in Mozambique to 4,359 per 10,000 in Uganda, while dyspnea ranged from 330 per 10,000 in Tanzania to 2,069 per 10,000 in Uganda (Fig. 1 A, B, D). Overall, countries such as Uganda and Burundi had the highest prevalence of both symptoms, whereas the lowest was observed in Kenya and Tanzania. In those countries, the highest frequency of clean fuel use was reported (Fig. 1 C). Factors associated with cough In the main model (Table 2 ), the odds of cough were 1.36 times higher (95% CI: 1.06–1.74) in urban households using unclean cooking fuel and 1.40 times higher (95% CI: 1.09–1.81) in rural households using unclean cooking fuel compared to urban households using clean cooking fuel. Being born in a care facility was significantly associated with a higher risk of cough. Higher odds for cough were observed in children under two years old compared to those 0–11 months old; in children who had small perceived birth size compared to average and large, being the mother's second, third, or fourth child compared to the first born child, and having a household head who was < 35 years old compared to these 35–55 years old. Also, higher odds of cough were associated with children of working mothers, of mothers who reported attending antenatal and postnatal care, children in wealthier households, and households with improved drinking water sources, whereas having electricity in the household decreased odds of cough (Table 2 and Supplementary Table 3). Supplementary Table 4 presents additional models, crude and partially adjusted, for cough. Table 2 Main model, multivariable logistic regression of factors linked to cough among children under two years old across ten East African countries, based on Demographic and Health Survey (DHS) 2015–2023. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) are provided. The factors shown were all included in the models. Those had a p-value of less than 0.2 in the bivariate analysis shown in Supplementary Table 2. Characteristics Cough aOR 95% CI Cooking fuel type and type of residence Clean-urban 1.00 Clean-rural 0.89 0.58–1.36 Unclean-urban 1.36 1.06–1.74 Unclean-rural 1.40 1.09–1.81 Age of child 0–11 months 1.00 12–23 months 1.16 1.09–1.22 Child’s weight at birth Underweight 1.00 Normal 1.03 0.94–1.13 Perceived size of child at birth Small 1.00 Average 0.73 0.68–0.80 Large 0.87 0.79–0.95 Birth order of child 1 1.00 2 to 4 0.91 0.84–0.97 5 and above 0.92 0.84–1.00 Mother's education level No education 1.00 Primary 1.07 0.99–1.16 Secondary 1.00 0.91–1.11 Higher 1.08 0.90–1.31 Mother's employment status No 1.00 Yes 1.54 1.44–1.64 Antenatal care visits Yes 1.00 No 0.83 0.71–0.96 Postnatal care visit No 1.00 Yes 1.06 1.00–1.14 Place of child’s delivery Home 1.00 Health Facility 1.14 1.05–1.23 Age of household head 55 years 0.95 0.86–1.06 Main source of drinking water Improved 1.00 Unimproved 0.81 0.74–0.88 Electricity in the household No 1.00 Yes 0.85 0.77–0.94 Factors associated with dyspnea In the main model, the odds of dyspnea (Table 3 ) were higher in urban households using unclean cooking fuel (aOR: 2.21; 95% CI: 1.49–3.28) and in rural unclean households (aOR: 3.08 95% CI: 2.07–4.58) compared to urban clean households. Having electricity in the household was associated with lower odds of dyspnea (aOR 0.73, 95% CI 0.64–0.83). Decreased odds of dyspnea were also associated with children perceived as having average or large size compared to small once. Children who were the mother's second, third, or fourth child were associated with a lower risk of dyspnea compared to being the firstborn. Higher odds of dyspnea were also associated with male sex of child, children of working mothers, those whose mothers attended antenatal and postnatal care, children in wealthier households, and households with access to improved drinking water sources (Table 3 and Supplementary Table 3). Supplementary Table 7 presents additional models, crude and partially adjusted, for dyspnea. Table 3 Main model, multivariable logistic regression of factors linked to dyspnea among children under two years old across ten East African countries, based on Demographic and Health Survey (DHS) 2015–2023. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) are provided. The factors shown were those included in the models. Those had a p-value < of less than 0.2 in the bivariate analysis shown in Supplementary Table 2. Characteristics Dyspnea aOR 95% CI Cooking fuel type and type of residence Clean-urban 1.00 Clean-rural 1.68 0.83–3.39 Unclean-urban 2.21 1.49–3.28 Unclean-rural 3.08 2.07–4.58 Sex of infant Male 1.00 Female 0.91 0.85–0.99 Infant’s weight at birth Underweight 1.00 Normal 0.96 0.85–1.09 Perceived size of infant at birth Small 1.00 Average 0.71 0.64–0.79 Large 0.93 0.89–0.95 Birth order of infant 1 1.00 2 to 4 0.86 0.77–0.95 5 and above 0.94 0.82–1.07 Mother's education level No education 1.00 Primary 0.99 0.89–1.09 Secondary 0.85 0.74–0.98 Higher 0.75 0.56–1.02 Mother's employment status No 1.00 Yes 1.49 1.36–1.63 Mother's marital status Never married, widowed, separated 1.00 Married and living with partner 1.17 0.98–1.33 Access to media Yes 1.00 No 1.09 0.91–1.19 Household wealth status Poor 1.00 Middle 1.01 0.91–1.13 Rich 1.30 1.16–1.47 Main source of drinking water Improved 1.00 Unimproved 0.77 0.69–0.86 Type of toilet facility Improved 1.00 Unimproved 1.09 0.99–1.13 Electricity in the household No 1.00 Yes 0.73 0.64–0.83 Discussion This cross-sectional study examined associations among household cooking fuel type, residential setting, and ARIs-related symptoms among children under 2 years old in 10 East African countries using recent DHS data. We found substantial variation in symptom prevalence across countries, with cough ranging from 13% to 44%, and dyspnea ranging from 3% to 21%. The highest prevalence of both symptoms was observed in Uganda and Burundi, while the lowest was found in Tanzania and Kenya, coinciding with higher reported use of clean cooking fuels. In the adjusted main model, children under 2 years old living in urban and rural households using unclean cooking fuels were at higher risk of cough and dyspnea than those in urban areas with clean cooking fuels. Children born with a larger birth size and having a middle-aged household head appeared to reduce the risk of both ARI-related symptoms. Children of mothers with employment, those from wealthier households, and children of mothers who did not attend antenatal are at a higher risk of ARI-related symptoms. Children aged 0–11 and born at a health facility were associated with a higher risk of cough, whereas having electricity in the household was linked with lower odds of dyspnea. This study suggests new regional insights on the prevalence of ARI-related symptoms, including cough and dyspnea, among children under 2 years old in East Africa. Importantly, there are no up-to-date estimates of ARI-related symptoms among children under two years in East Africa, as previous research has overwhelmingly focused on children under five. The meta-analytic evidence from 1990–2010 estimated that about 5% of infants under one year in sub-Saharan Africa experienced severe ARI episodes annually, the highest global incidence and significantly higher than in Europe or the Americas ( 7 ). More recent evidence remains limited to broad under-five age groups: a scoping review synthesizing 43 studies from 2000–2020 in sub-Saharan Africa found that the prevalence of lower respiratory infections among children under five years ranged from 2% to 60%, depending on the country, setting, and design (27). A pooled DHS analysis from 33 sub-Saharan countries reported that around 20% of children under five had cough or fever in the preceding two weeks ( 19 ). These wide variations across studies likely reflect differences in study populations, case definitions, healthcare access, and measurement approaches. Our findings suggest that exposure to household air pollution from unclean fuels remains an important contributor to ARIs-related symptoms in children under 2 years old across both residential settings. Similar to Amadu et al., we used an exposure variable combining fuel and urbanicity, and our findings align with this work, which included 31 sub-Saharan African countries and found that unclean fuel use increased the odds of ARI-related symptoms in urban and rural settings in children under 5 years old ( 21 ). In contrast, a study from Chad that examined cooking fuel type and urbanicity as separate exposures reported stronger associations between unclean fuel use and ARI-related symptoms in rural settings than in urban areas ( 20 ). However, treating fuel type and urbanicity as independent exposures may be misleading, as it fails to capture real-world exposure contexts. Urban children may face elevated ARI risk even when clean fuels are used, due to higher background exposure to ambient air pollution from traffic emissions, industrial activity, dense housing, waste burning, and reduced ventilation (28–31). In our study, 96% of East African households reported using unclean fuels, consistent with regional estimates of 88–90% (32). Wood remains the dominant fuel, followed by charcoal, with hotspots of solid-fuel dependence in Uganda, Burundi, Rwanda, Malawi, and parts of Mozambique and Ethiopia (32). The extremely high reliance on biomass fuels, combined with inadequate ventilation, likely contributes to the substantial ARI burden identified. Our results observed that children 12–23 months old experienced cough more frequently than those aged 0–11 months (infants). Also, dyspnea tends to be more frequently reported in infants. This is consistent with studies from the Philippines and Ethiopia showing that ARIs’ prevalence often peaks in the second year of life (33, 34). Although causality cannot be confirmed, the data suggest that both biological and behavioral factors: waning maternal antibodies, reduced breastfeeding, increased mobility and social contact, and greater exposure to household and environmental pathogens. We also observed a slightly lower risk of both studied ARI-related symptoms among female children, consistent with previous studies (20, 35), suggesting sex-linked immunological differences, including the advantage conferred by a second X chromosome (36). Our analysis showed that children perceived as smaller at birth exhibited more cough and dyspnea than those perceived as average or larger, reflecting well-established links between low birth weight, immune vulnerability, and susceptibility to respiratory infections. Birth size is a commonly used proxy for birthweight in DHS datasets ( 21 ) and shows consistent associations with adverse respiratory outcomes. Our findings suggested differences in reported ARI-related symptoms by birth order, however, the analysis did not evaluate the number or severity of those symptoms. In East African household contexts, birth order may reflect unmeasured caregiving practices, household composition, and resource allocation (37), rather than acting as an independent risk factor. Although direct evidence from low-income settings remains limited, studies from Europe and North America suggest that firstborn children may have a higher ARI-related symptoms due to less maternal caregiving experience and lower transplacental antibody transfer, whereas very high birth order may be associated with resource dilution (38). These mechanisms may help explain our findings in East Africa. Our data showed that a middle-aged household head, whether female or male, was associated with a lower risk of cough and dyspnea in infants up to two years old compared to both younger and older age group caregivers, which aligns with a previous study from Ethiopia (39). In sub-Saharan African contexts, middle-aged household heads may be more likely to have established livelihoods, stable household structures, and accumulated caregiving experience, which could indirectly influence infant health (40, 41). However, this interpretation should be considered cautious and exploratory rather than causal. Our results indicated that children of employed mothers were more likely to have cough and dyspnea symptoms in the last 2 weeks. Similar patterns have been documented in Ethiopia (34, 42) and from Pakistan (43). In our analysis, in the model adjusted only for maternal characteristics, having primary education compared with no education was also associated with higher reporting of both ARI-related symptoms. These findings should be interpreted cautiously, as they may reflect differences in symptom recognition, healthcare-seeking behavior, or reporting practices rather than true differences in disease risk. Previous studies have suggested potential mechanisms that may contribute to these patterns, including reduced time for direct maternal caregiving or breastfeeding (34) and increased exposure to respiratory pathogens through childcare attendance (44). However, the present study cannot evaluate these pathways because information on maternal occupation, breastfeeding practices, and the child’s daytime care arrangements was not available. Further research with detailed data on childcare, feeding practices, and maternal working conditions is needed to better understand these relationships in this context. An unexpected finding was that children whose mothers reported not attending antenatal care and postnatal visits had lower odds of both studied ARI-related symptoms, and children born at a care facility had higher odds of cough. However, this pattern has been reported in other sub-Saharan African studies and likely reflects confounding by healthcare-seeking behavior (45). Women who attend antenatal care often live in urban areas with higher ARI detection rates, may also report symptoms more accurately, and have better access to diagnosis (46). Thus, the observed associations probably reflect differential care-seeking and reporting patterns rather than actual protective effects of not receiving maternal healthcare. Relevance and recommendation Our study findings underscore the urgent need for targeted public health interventions to reduce children’s exposure to household air pollution. Strengthening policies that expand access to clean, affordable cooking fuels, alongside improvements in ventilation, safer stove designs, and community-level behavior change programs, could substantially reduce the burden of ARI-related symptoms among infants across East Africa. In addition to environmental interventions, there is a critical need to improve awareness and recognition of ARI-related symptoms. Future research should explore how maternal health literacy, cultural beliefs, and access to primary healthcare influence ARI-related symptoms recognition and the subsequent care-seeking pathways. Strengths and limitations A key strength of this study is the use of large, representative DHS datasets from 10 East African countries, which allow for robust cross-country comparisons and regional estimates for children under 2 years old—an age group for which evidence is particularly scarce. Harmonized data allowed for consistent assessment of household cooking fuel type and ARI-related symptoms across both urban and rural settings, providing insight into geographic disparities that are rarely examined. However, several limitations should be noted. ARI-related symptoms were based on maternal report rather than clinical diagnosis, which may have introduced recall bias or misclassification. We could not distinguish between upper and lower respiratory infections, nor assess the severity or duration of symptoms. Nonetheless, given that many infants in East Africa never reach formal healthcare facilities and get a diagnosis, community-reported symptoms may better reflect the broader burden than hospital-based data. Household cooking fuel use reflects a chronic exposure, whereas cough and dyspnea in the last 2 weeks capture acute symptoms, creating a temporal mismatch that limits causal inference. As a result, the observed associations may partly reflect concurrent infectious etiologies or short-term environmental exposures rather than direct effects of long-term household fuel use. Also, the cross-sectional data and adjusted associations reported in our study for child, maternal, and household characteristics should not be interpreted as causal effects. These variables were included to control for confounding of the primary exposure and to describe patterns in symptom reporting rather than to identify independent causal risk factors. Therefore, all covariate results should be interpreted cautiously and descriptively. In addition, our study did not include potentially relevant variables, such as breastfeeding status, nutritional intake, immunization status, micronutrient supplementation, outdoor air pollution, epidemic periods, or season of symptom onset, all of which are known to influence ARIs. Finally, DHS data may have a hierarchical structure; however, we did not account for this (for example, by clustering), which may affect the precision of our estimates. We do not have information on whether more than one child was included per household. Nevertheless, given that the analysis was restricted to children under 2 years old, it is less likely that multiple eligible children were present within the same household, and thus, potential intra-household correlation is expected to be limited. Conclusions The findings suggest that children under 2 years old living in households using unclean cooking fuels in both urban and rural areas are more likely to experience cough and dyspnea than those in urban, clean households. Beyond fuel type, our study identified several important risk factors associated with ARI-related symptoms being born smaller than average, and being the first, fifth, or later child of the mother. These results have direct policy relevance for advancing Sustainable Development Goal 7, universal access to clean energy, and suggest that interventions should prioritize expanding clean cooking fuels, improving household ventilation, and targeted caregiver education—particularly in high-burden urban and rural areas. Together, these actions could meaningfully reduce household air pollution exposure and improve respiratory health outcomes for young children in East Africa and other similar settings. Declarations Competing Interests The authors declare no competing interests. Patients and Public Involvement Statement It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research. Funding No any Author Contribution A.N.Y. designed the project, participated in data analysis, and drafted and finalized the manuscript.T.B.R participated in merging and cleaning the dataset and reviewed and provided input on the manuscriptK.K.S. reviewed and provided input on the manuscriptA-M.H reviewed and provided feedback on the manuscript.A.C.C reviewed and provided input on the manuscriptM.A.K. supervised the final analysis and methodology approach, contributed to the interpretation of results, and finalized the manuscript.All authors read and approved the final version of the manuscript. Acknowledgements We acknowledge the Demographic and Health Surveys program for authorizing the use of their datasets and data collectors in each country. Most importantly we greatly appreciate study participants in each country for their time. Data Availability The datasets used and analyzed during the current study is available from https://dhsprogram.com/data/DHS-Survey-Indicators-Characteristics-of-Households.cfm References Organization WH. Pneumonia in children https://www.who.int/news-room/fact-sheets/detail/pneumonia2022 [ Tekeba B, Gebrehana DA, Mekonnen EG, Zegeye AF, Mekonnen CK, Abate HK, et al. The comorbidities of diarrhea and acute respiratory tract infection and risk factors among under-five children in 45 low- and middle-income countries. Sci Rep. 2025;15(1):30139. Eric A. F. Simoes TC, Jeffrey Chow, Sonbol A. Shahid-Salles, Ramanan Laxminarayan, and T. Jacob John. Chapter 25Acute Respiratory Infections in Children. https://www.ncbi.nlm.nih.gov/books/NBK11786/2006. Organization. WH. Integrated global action plan for prevention and control of pneumonia and diarrhoea (GAPPD). . Geneva; 2013. . Zhu H, Huang K, Han X, Pan Z, Cheng H, Wang Q, et al. The burden of acute respiratory infection in children under 5 attributable to economic inequality in low- and middle-income countries. BMJ Glob Health. 2025;10(3). Chilot D, Shitu K, Gela YY, Getnet M, Mulat B, Diress M, et al. Factors associated with healthcare-seeking behavior for symptomatic acute respiratory infection among children in East Africa: a cross-sectional study. BMC Pediatr. 2022;22(1):662. Nair H, Simoes EA, Rudan I, Gessner BD, Azziz-Baumgartner E, Zhang JSF, et al. Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis. Lancet. 2013;381(9875):1380-90. Dherani M, Pope D, Mascarenhas M, Smith KR, Weber M, Bruce N. Indoor air pollution from unprocessed solid fuel use and pneumonia risk in children aged under five years: a systematic review and meta-analysis. Bull World Health Organ. 2008;86(5):390-8C. Andualem Z, Nigussie Azene Z, Dessie A, Dagne H, Dagnew B. Acute respiratory infections among under-five children from households using biomass fuel in Ethiopia: systematic review and meta-analysis. Multidiscip Respir Med. 2020;15(1):710. WHO. Defining clean fuels and technologies. https://www.who.int/tools/clean-household-energy-solutions-toolkit/module-7-defining-clean. 2025 [ Smith KR, Bruce N, Balakrishnan K, Adair-Rohani H, Balmes J, Chafe Z, et al. Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution. Annu Rev Public Health. 2014;35:185-206. Fandino-Del-Rio M, Kephart JL, Williams KN, Moulton LH, Steenland K, Checkley W, et al. Household air pollution exposure and associations with household characteristics among biomass cookstove users in Puno, Peru. Environ Res. 2020;191:110028. Hoffmann B, Boogaard H, de Nazelle A, Andersen ZJ, Abramson M, Brauer M, et al. WHO Air Quality Guidelines 2021-Aiming for Healthier Air for all: A Joint Statement by Medical, Public Health, Scientific Societies and Patient Representative Organisations. Int J Public Health. 2021;66:1604465. Kilbo Edlund K, Kisiel, M.A., Asker, C. et al. . High-resolution dispersion modelling of PM2.5, PM10, NOx and NO2 exposure in metropolitan areas in Sweden 2000‒2018 – large health gains due to decreased population exposure. Air Qual Atmos Health 17. 2024;17:1661–75 Tondel M, Kisiel MA, Barregard L, Dahlquist M, Edlund KK, Eriksson C, et al. Metabolic syndrome in the SCAPIS cohort - Investigating associations at low level exposure to ambient air pollution. Sci Total Environ. 2025;995:180120. Trachsel D, Erb TO, Hammer J, von Ungern-Sternberg BS. Developmental respiratory physiology. Paediatr Anaesth. 2022;32(2):108-17. Africa. EAiEaS. World Bank https://www.worldbank.org/en/region/afr/brief/afe-energy. 2025 [ Midulla EEF. ERS Handbook of Paediatric Respiratory Medicine. European Respiratory Sociaty2021. Fenta HM, Zewotir TT, Naidoo S, Naidoo RN, Mwambi H. Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches. Sci Rep. 2024;14(1):15801. Aremu O, Aremu OO. Effect of Household Air Pollution and Neighbourhood Deprivation on the Risk of Acute Respiratory Infection Among Under-Five Children in Chad: A Multilevel Analysis. Int J Environ Res Public Health. 2025;22(5). Amadu I, Seidu AA, Mohammed A, Duku E, Miyittah MK, Ameyaw EK, et al. Assessing the combined effect of household cooking fuel and urbanicity on acute respiratory symptoms among under-five years in sub-Saharan Africa. Heliyon. 2023;9(6):e16546. ICF. International. Demographic and Health Survey Sampling and Household Listing Manual. . 2012. DHS. Guide to DHS Statistics. https://www.dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm. West KP, Sznajder KK, Hwang G, Sauve HE, Roba KT, Baatiema L, et al. Unclean cooking fuel use and stillbirth in Ghana: evidence from the 2022 DHS. Front Glob Womens Health. 2025;6:1636924. Emery K, Studer M, Berchtold A. Comparison of imputation methods for univariate categorical longitudinal data. Qual Quant. 2025;59(2):1767-91. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1):125-37. Sarfo JO, Amoadu M, Gyan TB, Osman AG, Kordorwu PY, Adams AK, et al. Acute lower respiratory infections among children under five in Sub-Saharan Africa: a scoping review of prevalence and risk factors. BMC Pediatr. 2023;23(1):225. Khalequzzaman M, Kamijima M, Sakai K, Ebara T, Hoque BA, Nakajima T. Indoor air pollution and health of children in biomass fuel-using households of Bangladesh: comparison between urban and rural areas. Environ Health Prev Med. 2011;16(6):375-83. Carter E, Norris C, Dionisio KL, Balakrishnan K, Checkley W, Clark ML, et al. Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter. Environ Health Perspect. 2017;125(7):076002. Masekela R, Vanker A. Lung Health in Children in Sub-Saharan Africa: Addressing the Need for Cleaner Air. Int J Environ Res Public Health. 2020;17(17). Murray EL, Brondi L, Kleinbaum D, McGowan JE, Van Mels C, Brooks WA, et al. Cooking fuel type, household ventilation, and the risk of acute lower respiratory illness in urban Bangladeshi children: a longitudinal study. Indoor Air. 2012;22(2):132-9. Yitageasu G, Tesfaye AH, Worede EA, Kifle T, Tigabie M, Gizaw Z, et al. Determinants and spatial patterns of solid fuel use in East Africa based on demographic and health survey data from 2012 to 2023. Sci Rep. 2025;15(1):28142. Otani K, Saito M, Okamoto M, Tamaki R, Saito-Obata M, Kamigaki T, et al. Incidence of lower respiratory tract infection and associated viruses in a birth cohort in the Philippines. BMC Infect Dis. 2022;22(1):313. Amsalu ET, Akalu TY, Gelaye KA. Spatial distribution and determinants of acute respiratory infection among under-five children in Ethiopia: Ethiopian Demographic Health Survey 2016. PLoS One. 2019;14(4):e0215572. Rahman A, Hossain MM. Prevalence and determinants of fever, ARI and diarrhea among children aged 6-59 months in Bangladesh. BMC Pediatr. 2022;22(1):117. Feng Z, Liao M, Zhang L. Sex differences in disease: sex chromosome and immunity. J Transl Med. 2024;22(1):1150. Bras H, Mandemakers J. Maternal education and sibling inequalities in child nutritional status in Ethiopia. SSM Popul Health. 2022;17:101041. Bali VA, & Guo, J. . Birth order and child health (IFAU Working Paper 2017:16). . Institute for Evaluation of Labour Market and Education Policy (IFAU). https://www.ifau.se/globalassets/pdf/se/2017/wp2017-16-birth_order_and_child_health.pdf; 2017. Geremew BM, Gelaye KA, Melesse AW, Akalu TY, Baraki AG. Factors Affecting Under-Five Mortality in Ethiopia: A Multilevel Negative Binomial Model. Pediatric Health Med Ther. 2020;11:525-34. Akinyemi JO, Banda P, De Wet N, Akosile AE, Odimegwu CO. Household relationships and healthcare seeking behaviour for common childhood illnesses in sub-Saharan Africa: a cross-national mixed effects analysis. BMC Health Serv Res. 2019;19(1):308. Ashira Menashe-Oren PB, Carren Ginsburg, Yacouba Compaore, Mark Collinson. The dynamic role of household structure on under-5 mortality in southern and eastern subSaharan Africa. https://www.demographic-research.org/volumes/vol49/11/49-11.pdf; 2023. Merera AM. Determinants of acute respiratory infection among under-five children in rural Ethiopia. BMC Infect Dis. 2021;21(1):1203. Fatmi Z, White F. A comparison of 'cough and cold' and pneumonia: risk factors for pneumonia in children under 5 years revisited. Int J Infect Dis. 2002;6(4):294-301. DeJonge PM, Monto AS, Malosh RE, Petrie JG, Callear A, Segaloff HE, et al. Comparing the Etiology of Viral Acute Respiratory Illnesses Between Children Who Do and Do Not Attend Childcare. Pediatr Infect Dis J. 2023;42(6):443-8. Ahmed KY, Dadi AF, Kibret GD, Bizuayehu HM, Hassen TA, Amsalu E, et al. Population modifiable risk factors associated with under-5 acute respiratory tract infections and diarrhoea in 25 countries in sub-Saharan Africa (2014-2021): an analysis of data from demographic and health surveys. EClinicalMedicine. 2024;68:102444. Asresie MB, Y.; Vicendese, D.; Batra, M.; Erbas, B. . The Effect of Maternal Antenatal Care Utilisation on Childhood Acute Respiratory Infection: A Systematic Review and Meta-Analysis. . Int J Environ Res Public Health 2025;22:1627. Additional Declarations No competing interests reported. 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D) The prevalence of cough and dyspnea per 10,000 in each country. Data from the Demographic and Health Survey (DHS) 2015–2023. Spatial distribution of (C) cough, and (D) dyspnea.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8920900/v1/9c69be2566f7aacbc1f57a2e.jpeg"},{"id":104398610,"identity":"33934c20-f9be-42a2-9da8-b73df6d3b20b","added_by":"auto","created_at":"2026-03-11 12:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2166309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8920900/v1/4a438f74-3142-42e4-b21e-4c5c8a8ef411.pdf"},{"id":103602365,"identity":"e0920603-550d-42d8-a4a0-e55bbd6bd2bd","added_by":"auto","created_at":"2026-02-27 14:14:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":40581,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8920900/v1/34ba088fb63483c1dcd03d27.docx"},{"id":103602366,"identity":"78cc6515-a5d1-4cb3-b444-6a98cf7d5f6c","added_by":"auto","created_at":"2026-02-27 14:14:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18025,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8920900/v1/067eb0d2c9e60aa78ee9a5af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Urbanicity–Cooking Fuel Type and Acute Respiratory Infection Symptoms Among Children Under Two Years across Ten East African countries. ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory infections (ARIs) remain one of the leading causes of morbidity and mortality among children under five years of age, with even younger children being particularly vulnerable (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). ARIs encompass infections of both upper and lower respiratory tract (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Globally, an estimated 1.3\u0026nbsp;million children under five die from ARIs each year (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), with the overwhelming majority of deaths occurring in low-and middle-income countries (LMICs) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In East Africa,, ARIs contribute significantly to childhood morbidity and mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), exacerbating existing socioeconomic inequities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExposure to household air pollution from the combustion of unclean cooking fuels is a major and potentially modifiable risk factor for ARIs in children (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). According to the World Health Organization (WHO), unclean cooking fuels are unprocessed coal, kerosene, and biomass fuels (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Common sources of cooking fuel include unprocessed coal, kerosene, and a range of biomass fuels such as wood and agricultural byproducts (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Burning these fuels in poorly ventilated kitchens or inefficient stoves releases high concentrations of respirable particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and toxic gases. Kitchens using biomass fuels in LMICs commonly reach 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e levels of 200\u0026ndash;3,000 \u0026micro;g/m\u0026sup3; (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), far exceeding the WHO guidelines values being no more than 5 \u0026micro;g/m\u0026sup3; and a 24-hour mean of 15 \u0026micro;g/m\u0026sup3; (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). PM\u003csub\u003e2.5\u003c/sub\u003e penetrates deep into the respiratory tract, triggering inflammation and ARIs (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Children are particularly susceptible due to immature lungs, higher ventilation rates relative to body size, proximity to cooking areas, and increased exposure to infectious agents (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore than half of the world\u0026rsquo;s unelectrified population lives in Eastern and Southern Africa, and nearly a quarter of those globally lacking access to clean cooking fuel also reside in this region. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). As a result, reliance on solid biomass fuels remains pervasive. Gendered household roles further increase exposure: women and young children\u0026mdash;often carried during cooking\u0026mdash;experience disproportionately high indoor pollution levels (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies examining household energy use and ARIs have primarily focused on children under five years of age and fuel type alone. Country-level analyses using Demographic and Health Survey (DHS) or government-administered survey data consistently show increases ARI-related symptom risk associated with unclean fuel use (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additional determinants include younger age, teenage motherhood, low maternal education, poverty, limited access to media, low birth weight, absence of breastfeeding, deficient water and sanitation access, and poor household conditions (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the high burden of ARIs and household air pollution in East Africa, few regional analyses have examined how urbanicity modifies the association between cooking fuel type and ARI-related symptoms risk\u0026mdash;especially among children under two or one year of age (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these evidence gaps, we aimed to examine the associations between urbanicity, household cooking fuel type, and symptoms among children under two years old across ten East African countries. Using pooled nationally representative DHS data, the analysis aims to generate regional evidence while accounting for key confounders, including socio-demographic, household, and environmental factors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and data source\u003c/h2\u003e \u003cp\u003eEast Africa comprises 19 countries according to the United Nations classification. This study included ten East African countries with Demographic and Health Survey (DHS) data collected between 2015 and 2023 (Burundi, Comoros, Djibouti, Ethiopia, Eritrea, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Somaliland, Tanzania, Uganda, Zambia, and Zimbabwe); countries without DHS surveys (Djibouti, Somalia, Somaliland, the Seychelles, Mauritius, and the R\u0026eacute;union) or with surveys conducted before 2015 (Eritrea, Comoros, and Zimbabwe) were excluded. DHS data were obtained from the official DHS Program database following approval. Appendix Table\u0026nbsp;1 shows the year of the survey in the included countries. DHS surveys use nationally representative, stratified, multistage sampling and collect data from women aged 15\u0026ndash;49 years (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe cross-sectional data from the Children\u0026rsquo;s Recode, drawn from the Women\u0026rsquo;s Questionnaire (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The analytic sample included 38,849 women with children under two years old.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthic statement\u003c/h3\u003e\n\u003cp\u003eThe DHS Program obtains ethical approval from the Institutional Review Board and national ethics committees. All DHS surveys follow ethical protocols like informed consent, confidentiality, and anonymity. Since this analysis used de-identified secondary data, no further ethical approval was needed. Access was granted via the DHS data request process. All analyses adhered to DHS Terms of Use and research guidelines.\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eThe outcome variables in this study were ARI-related symptoms, including cough and dyspnea (short rapid breath) in the last 2 weeks. The symptoms were reported by the child's mother. Both variables were analyzed separately and coded as binary outcomes.\u003c/p\u003e \u003cp\u003eThe cough variable was derived from the survey question asking whether the child had had a cough in the last 2 weeks (h31). Cough was recoded into a dichotomous variable indicating whether the child had experienced cough (\u0026ldquo;Yes\u0026rdquo; encoded as 1) or not (\u0026ldquo;No\u0026rdquo; encoded as 0). The variable dyspnea (short rapid breath) originates from the question in the survey inquiring whether the child had suffered from rapid breathing in the last 2 weeks (h31b), which was left as the original yes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) or no (0) response. The outcome variables, cough and dyspnea, were analyzed separately.\u003c/p\u003e\n\u003ch3\u003eExposure variables\u003c/h3\u003e\n\u003cp\u003eCooking fuel was categorized as clean or unclean based on WHO definitions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Urbanicity was classified as urban or rural. These variables were combined to create four exposure categories: urban-clean, urban-unclean, rural-clean, and rural-unclean (24).\u003c/p\u003e \u003cp\u003eType of cooking fuel: The exposure variables in this study were derived from the variables' type of cooking fuel' and 'type of residence' The cooking fuel type was recoded as clean (electric, biogas, natural gas, and liquefied petroleum gas) and unclean (coal/lignite, kerosene, charcoal, wood, straw/shrubs/grass, agricultural crop, and animal dung).\u003c/p\u003e \u003cp\u003eResidences: Type of residence or \u0026ldquo;urbanicity\u0026rdquo; was recoded into urban and rural, while responses indicating abroad dropped. These two variables were used to create the exposure variable for this study: the type of cooking fuel used.\u003c/p\u003e \u003cp\u003eTo create the exposure variable for this study, four mutually exclusive categories were defined: urban-clean, urban-unclean, rural-clean, and rural-unclean.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates were chosen based on existing literature indicating they could influence the independent and dependent variables and potentially confound the results. When examining the relationship between ARI-related symptoms and urbanicity, other potential confounding variables from the existing literature were included in the analyses (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These variables fell into three categories: child-, mother-, and household characteristics (summarized in Appendix 1: Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analysis was conducted using R (version 4.0.5; R Core Team, 2021). We considered statistical significance at p\u0026thinsp;\u003cb\u003e\u0026lt;\u003c/b\u003e\u0026thinsp;0.05. The svydesign() function was used to ensure that survey design information was incorporated into the analyses. Geospatial analyses and visualization were conducted in R and ArcGIS Online. R was used to calculate the prevalence of ARI-related symptoms and clean cooking fuel by country. The prevalence of ARI-related symptoms and clean cooking fuel use was calculated by dividing the number of cases by the total number of responses, then multiplying by 10,000.\u003c/p\u003e \u003cp\u003eMissing responses regarding cough and dyspnea, and \u0026ldquo;I don\u0026rsquo;t know\u0026rdquo; responses made up less than 10% of total responses and were therefore removed. Missing responses, \u0026ldquo;abroad\u0026rdquo; responses for the type of residence variable, and \u0026ldquo;No food cooked in house\u0026rdquo;, \u0026ldquo;Other\u0026rdquo;, and \u0026ldquo;Not a de jure resident\u0026rdquo; responses to the kind of cooking fuel were removed. Missing responses for covariates were dropped if they accounted for less than 10% of the data. The variable \u0026lsquo;water source\u0026rsquo;, missing over 10% of responses, was imputed using the K-nearest neighbors\u0026rsquo; approach (R function kNN(); kNN imputation estimates missing values based on the most similar observations in the dataset, making it suitable for categorical variables (25). Similarity between observations was determined using an appropriate distance metric, and the most frequent category among the \u003cem\u003ek\u003c/em\u003e nearest neighbors was assigned. The number of neighbors was set to \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 to balance local similarity and robustness of the estimates.Variable responses such as \u0026ldquo;don\u0026rsquo;t know\u0026rdquo;, \u0026ldquo;abroad\u0026rdquo;, \u0026ldquo;other\u0026rdquo;, \u0026ldquo;not a de jure resident\u0026rdquo; were removed for the analyses.\u003c/p\u003e \u003cp\u003eBivariate analysis was conducted using a chi-square test of independence to examine associations between each outcome variable and each exposure and covariate. To further explore the relationships among cough, dyspnea, and cooking-fuel type, logistic regression models were fitted for each outcome variable. The main model, stratified by cough and dyspnea, was adjusted for all variables with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in the bivariate analysis. Previous studies found that liberal inclusion criteria, such as p\u0026thinsp;\u0026lt;\u0026thinsp;0.2, help avoid missing important predictors (26). To ensure survey weights were accounted for, the svyglm() function with a quasibinomial family was used for these regressions. Weighted odds ratios (ORs) and 95% confidence intervals (CIs) were reported. The additional models included a crude model (Model 1), a model adjusted only for the child\u0026rsquo;s characteristics (Model 2), a model adjusted only for the mother\u0026rsquo;s characteristics (Model 3), and a model adjusted only for household characteristics (Model 4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBasic characteristics of the study population\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the basic characteristics of 38,849 mother\u0026ndash;child pairs across 10 East African countries. Most households (96%) used unclean cooking fuels, and 79% of these were in rural areas. Only 4% of households reported using clean fuels, predominantly in urban settings. Slightly more than half of the children (52%) were aged 0\u0026ndash;11 months, and an equal distribution by sex (50% males). More than three-quarters of mothers delivered in health facilities (77%), and a large proportion (73%) did not receive postnatal care. The number of missing variables is shown in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic child (under two years old), maternal, and household characteristics stratified by symptom, such as cough and dyspnea, both or neither of symptoms across ten East African countries, 2015\u0026ndash;2023 Demographic and Health Survey (DHS) data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeither\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;38,849\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;7,237\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;796\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,902\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;26,914\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCooking fuel and residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,309 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,008 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,580 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,362 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e488 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,638 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,651 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,574 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e699 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,354 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,025 (78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;11 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,228 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,490 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,973 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,344 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;23 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,621 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,746 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,929 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,570 (47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of Child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,517 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,627 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,045 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,440 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,332 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,610 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e392 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,857 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,474 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild\u0026rsquo;s weight at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,117 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e818 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e454 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,754 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34,732 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,419 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e706 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,448 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,160 (90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived size of child at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,653 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,354 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e817 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,314 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,888 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,706 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,822 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,012 (56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,308 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,176 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,264 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,588 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,216 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,833 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e942 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,262 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,164 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,638 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,795 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,345 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,469 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,767 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,165 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,307 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,666 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,332 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e961 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,194 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,730 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,785 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,118 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,386 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,856 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,754 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e730 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,215 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,597 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e366 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,119 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's employment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,117 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,296 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,178 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,404 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,732 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,941 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e557 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,724 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,510 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s age at first birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,703 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,099 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,317 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,814 (59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,146 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,138 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,585 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,100 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's Marital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married, widowed, separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,967 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,215 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e550 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,105 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried and living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,882 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,022 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e699 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,352 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22,809 (85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntenatal care visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36,161 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,950 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e765 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,613 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,833 (92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,688 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e289 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,081 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostnatal care visit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,246 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,168 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e589 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,812 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,676 (73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,603 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,068 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,090 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,238 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of child\u0026rsquo;s delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,063 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,339 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e962 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,586 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,786 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,898 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e620 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,940 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,328 (76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,600 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,193 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e790 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,882 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,735 (99%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoesn't smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,600 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,193 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e790 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,882 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,735 (99%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvery day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of household head\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,291 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,723 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,984 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,168 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,947 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,839 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e328 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,559 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,221 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,611 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e675 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e359 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,526 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of household head\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,737 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,649 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e628 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,105 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,355 (79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,112 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,588 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e797 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,559 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,889 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,616 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e530 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,647 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,960 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,621 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,372 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,268 (79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,274 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,052 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,877 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,986 (45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,585 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,432 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e756 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,225 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,990 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,752 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,269 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,703 (36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain source of drinking water\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,434 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,949 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e655 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,106 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,723 (77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,415 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,287 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e796 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,191 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of toilet facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,879 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,704 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,740 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,059 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,970 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,532 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,162 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,855 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectricity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,163 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,334 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,236 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,939 (74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,686 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,903 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e666 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,975 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccess to media\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,312 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,618 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,658 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,692 (47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,537 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,619 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,244 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,222 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: \u0026lowast;A difference of one observation between the maternal smoking variables (N\u0026thinsp;=\u0026thinsp;38,850) and the total analytic sample (N\u0026thinsp;=\u0026thinsp;38,849) reflects a case with complete maternal smoking data but missing information in another covariate before imputation or analytic dataset construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of cough and dyspnea in infants\u003c/h2\u003e \u003cp\u003eThe prevalence of cough in children under two years old ranged from 1,267 per 10,000 children in Mozambique to 4,359 per 10,000 in Uganda, while dyspnea ranged from 330 per 10,000 in Tanzania to 2,069 per 10,000 in Uganda (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B, D). Overall, countries such as Uganda and Burundi had the highest prevalence of both symptoms, whereas the lowest was observed in Kenya and Tanzania. In those countries, the highest frequency of clean fuel use was reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with cough\u003c/h2\u003e \u003cp\u003eIn the main model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the odds of cough were 1.36 times higher (95% CI: 1.06\u0026ndash;1.74) in urban households using unclean cooking fuel and 1.40 times higher (95% CI: 1.09\u0026ndash;1.81) in rural households using unclean cooking fuel compared to urban households using clean cooking fuel.\u003c/p\u003e \u003cp\u003eBeing born in a care facility was significantly associated with a higher risk of cough.\u003c/p\u003e \u003cp\u003eHigher odds for cough were observed in children under two years old compared to those 0\u0026ndash;11 months old; in children who had small perceived birth size compared to average and large, being the mother's second, third, or fourth child compared to the first born child, and having a household head who was \u0026lt;\u0026thinsp;35 years old compared to these 35\u0026ndash;55 years old. Also, higher odds of cough were associated with children of working mothers, of mothers who reported attending antenatal and postnatal care, children in wealthier households, and households with improved drinking water sources, whereas having electricity in the household decreased odds of cough (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;3). Supplementary Table\u0026nbsp;4 presents additional models, crude and partially adjusted, for cough.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain model, multivariable logistic regression of factors linked to cough among children under two years old across ten East African countries, based on Demographic and Health Survey (DHS) 2015\u0026ndash;2023. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) are provided. The factors shown were all included in the models. Those had a p-value of less than 0.2 in the bivariate analysis shown in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking fuel type and type of residence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.06\u0026ndash;1.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.09\u0026ndash;1.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;11 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;23 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.09\u0026ndash;1.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild\u0026rsquo;s weight at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived size of child at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.68\u0026ndash;0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.79\u0026ndash;0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.84\u0026ndash;0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's employment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.44\u0026ndash;1.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntenatal care visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.71\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostnatal care visit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.00\u0026ndash;1.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of child\u0026rsquo;s delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.05\u0026ndash;1.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of household head\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.85\u0026ndash;0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain source of drinking water\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.74\u0026ndash;0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectricity in the household\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with dyspnea\u003c/h2\u003e \u003cp\u003eIn the main model, the odds of dyspnea (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were higher in urban households using unclean cooking fuel (aOR: 2.21; 95% CI: 1.49\u0026ndash;3.28) and in rural unclean households (aOR: 3.08 95% CI: 2.07\u0026ndash;4.58) compared to urban clean households. Having electricity in the household was associated with lower odds of dyspnea (aOR 0.73, 95% CI 0.64\u0026ndash;0.83). Decreased odds of dyspnea were also associated with children perceived as having average or large size compared to small once. Children who were the mother's second, third, or fourth child were associated with a lower risk of dyspnea compared to being the firstborn. Higher odds of dyspnea were also associated with male sex of child, children of working mothers, those whose mothers attended antenatal and postnatal care, children in wealthier households, and households with access to improved drinking water sources (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;3). Supplementary Table\u0026nbsp;7 presents additional models, crude and partially adjusted, for dyspnea.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain model, multivariable logistic regression of factors linked to dyspnea among children under two years old across ten East African countries, based on Demographic and Health Survey (DHS) 2015\u0026ndash;2023. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) are provided. The factors shown were those included in the models. Those had a p-value\u0026thinsp;\u0026lt;\u0026thinsp;of less than 0.2 in the bivariate analysis shown in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking fuel type and type of residence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.49\u0026ndash;3.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclean-rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.07\u0026ndash;4.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of infant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.85\u0026ndash;0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfant\u0026rsquo;s weight at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived size of infant at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.64\u0026ndash;0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.89\u0026ndash;0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order of infant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.77\u0026ndash;0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's employment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.36\u0026ndash;1.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's marital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married, widowed, separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried and living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccess to media\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.16\u0026ndash;1.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain source of drinking water\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.69\u0026ndash;0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of toilet facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectricity in the household\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.64\u0026ndash;0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study examined associations among household cooking fuel type, residential setting, and ARIs-related symptoms among children under 2 years old in 10 East African countries using recent DHS data. We found substantial variation in symptom prevalence across countries, with cough ranging from 13% to 44%, and dyspnea ranging from 3% to 21%. The highest prevalence of both symptoms was observed in Uganda and Burundi, while the lowest was found in Tanzania and Kenya, coinciding with higher reported use of clean cooking fuels.\u003c/p\u003e \u003cp\u003eIn the adjusted main model, children under 2 years old living in urban and rural households using unclean cooking fuels were at higher risk of cough and dyspnea than those in urban areas with clean cooking fuels. Children born with a larger birth size and having a middle-aged household head appeared to reduce the risk of both ARI-related symptoms. Children of mothers with employment, those from wealthier households, and children of mothers who did not attend antenatal are at a higher risk of ARI-related symptoms. Children aged 0\u0026ndash;11 and born at a health facility were associated with a higher risk of cough, whereas having electricity in the household was linked with lower odds of dyspnea.\u003c/p\u003e \u003cp\u003eThis study suggests new regional insights on the prevalence of ARI-related symptoms, including cough and dyspnea, among children under 2 years old in East Africa. Importantly, there are no up-to-date estimates of ARI-related symptoms among children under two years in East Africa, as previous research has overwhelmingly focused on children under five. The meta-analytic evidence from 1990\u0026ndash;2010 estimated that about 5% of infants under one year in sub-Saharan Africa experienced severe ARI episodes annually, the highest global incidence and significantly higher than in Europe or the Americas (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). More recent evidence remains limited to broad under-five age groups: a scoping review synthesizing 43 studies from 2000\u0026ndash;2020 in sub-Saharan Africa found that the prevalence of lower respiratory infections among children under five years ranged from 2% to 60%, depending on the country, setting, and design (27). A pooled DHS analysis from 33 sub-Saharan countries reported that around 20% of children under five had cough or fever in the preceding two weeks (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These wide variations across studies likely reflect differences in study populations, case definitions, healthcare access, and measurement approaches.\u003c/p\u003e \u003cp\u003eOur findings suggest that exposure to household air pollution from unclean fuels remains an important contributor to ARIs-related symptoms in children under 2 years old across both residential settings. Similar to Amadu et al., we used an exposure variable combining fuel and urbanicity, and our findings align with this work, which included 31 sub-Saharan African countries and found that unclean fuel use increased the odds of ARI-related symptoms in urban and rural settings in children under 5 years old (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In contrast, a study from Chad that examined cooking fuel type and urbanicity as separate exposures reported stronger associations between unclean fuel use and ARI-related symptoms in rural settings than in urban areas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, treating fuel type and urbanicity as independent exposures may be misleading, as it fails to capture real-world exposure contexts. Urban children may face elevated ARI risk even when clean fuels are used, due to higher background exposure to ambient air pollution from traffic emissions, industrial activity, dense housing, waste burning, and reduced ventilation (28\u0026ndash;31).\u003c/p\u003e \u003cp\u003eIn our study, 96% of East African households reported using unclean fuels, consistent with regional estimates of 88\u0026ndash;90% (32). Wood remains the dominant fuel, followed by charcoal, with hotspots of solid-fuel dependence in Uganda, Burundi, Rwanda, Malawi, and parts of Mozambique and Ethiopia (32). The extremely high reliance on biomass fuels, combined with inadequate ventilation, likely contributes to the substantial ARI burden identified.\u003c/p\u003e \u003cp\u003eOur results observed that children 12\u0026ndash;23 months old experienced cough more frequently than those aged 0\u0026ndash;11 months (infants). Also, dyspnea tends to be more frequently reported in infants. This is consistent with studies from the Philippines and Ethiopia showing that ARIs\u0026rsquo; prevalence often peaks in the second year of life (33, 34). Although causality cannot be confirmed, the data suggest that both biological and behavioral factors: waning maternal antibodies, reduced breastfeeding, increased mobility and social contact, and greater exposure to household and environmental pathogens. We also observed a slightly lower risk of both studied ARI-related symptoms among female children, consistent with previous studies (20, 35), suggesting sex-linked immunological differences, including the advantage conferred by a second X chromosome (36).\u003c/p\u003e \u003cp\u003eOur analysis showed that children perceived as smaller at birth exhibited more cough and dyspnea than those perceived as average or larger, reflecting well-established links between low birth weight, immune vulnerability, and susceptibility to respiratory infections. Birth size is a commonly used proxy for birthweight in DHS datasets (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and shows consistent associations with adverse respiratory outcomes. Our findings suggested differences in reported ARI-related symptoms by birth order, however, the analysis did not evaluate the number or severity of those symptoms. In East African household contexts, birth order may reflect unmeasured caregiving practices, household composition, and resource allocation (37), rather than acting as an independent risk factor. Although direct evidence from low-income settings remains limited, studies from Europe and North America suggest that firstborn children may have a higher ARI-related symptoms due to less maternal caregiving experience and lower transplacental antibody transfer, whereas very high birth order may be associated with resource dilution (38). These mechanisms may help explain our findings in East Africa.\u003c/p\u003e \u003cp\u003eOur data showed that a middle-aged household head, whether female or male, was associated with a lower risk of cough and dyspnea in infants up to two years old compared to both younger and older age group caregivers, which aligns with a previous study from Ethiopia (39). In sub-Saharan African contexts, middle-aged household heads may be more likely to have established livelihoods, stable household structures, and accumulated caregiving experience, which could indirectly influence infant health (40, 41). However, this interpretation should be considered cautious and exploratory rather than causal.\u003c/p\u003e \u003cp\u003eOur results indicated that children of employed mothers were more likely to have cough and dyspnea symptoms in the last 2 weeks. Similar patterns have been documented in Ethiopia (34, 42) and from Pakistan (43). In our analysis, in the model adjusted only for maternal characteristics, having primary education compared with no education was also associated with higher reporting of both ARI-related symptoms. These findings should be interpreted cautiously, as they may reflect differences in symptom recognition, healthcare-seeking behavior, or reporting practices rather than true differences in disease risk. Previous studies have suggested potential mechanisms that may contribute to these patterns, including reduced time for direct maternal caregiving or breastfeeding (34) and increased exposure to respiratory pathogens through childcare attendance (44). However, the present study cannot evaluate these pathways because information on maternal occupation, breastfeeding practices, and the child\u0026rsquo;s daytime care arrangements was not available. Further research with detailed data on childcare, feeding practices, and maternal working conditions is needed to better understand these relationships in this context.\u003c/p\u003e \u003cp\u003eAn unexpected finding was that children whose mothers reported not attending antenatal care and postnatal visits had lower odds of both studied ARI-related symptoms, and children born at a care facility had higher odds of cough. However, this pattern has been reported in other sub-Saharan African studies and likely reflects confounding by healthcare-seeking behavior (45). Women who attend antenatal care often live in urban areas with higher ARI detection rates, may also report symptoms more accurately, and have better access to diagnosis (46). Thus, the observed associations probably reflect differential care-seeking and reporting patterns rather than actual protective effects of not receiving maternal healthcare.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelevance and recommendation\u003c/h2\u003e \u003cp\u003eOur study findings underscore the urgent need for targeted public health interventions to reduce children\u0026rsquo;s exposure to household air pollution. Strengthening policies that expand access to clean, affordable cooking fuels, alongside improvements in ventilation, safer stove designs, and community-level behavior change programs, could substantially reduce the burden of ARI-related symptoms among infants across East Africa. In addition to environmental interventions, there is a critical need to improve awareness and recognition of ARI-related symptoms. Future research should explore how maternal health literacy, cultural beliefs, and access to primary healthcare influence ARI-related symptoms recognition and the subsequent care-seeking pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA key strength of this study is the use of large, representative DHS datasets from 10 East African countries, which allow for robust cross-country comparisons and regional estimates for children under 2 years old\u0026mdash;an age group for which evidence is particularly scarce. Harmonized data allowed for consistent assessment of household cooking fuel type and ARI-related symptoms across both urban and rural settings, providing insight into geographic disparities that are rarely examined.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be noted. ARI-related symptoms were based on maternal report rather than clinical diagnosis, which may have introduced recall bias or misclassification. We could not distinguish between upper and lower respiratory infections, nor assess the severity or duration of symptoms. Nonetheless, given that many infants in East Africa never reach formal healthcare facilities and get a diagnosis, community-reported symptoms may better reflect the broader burden than hospital-based data.\u003c/p\u003e \u003cp\u003eHousehold cooking fuel use reflects a chronic exposure, whereas cough and dyspnea in the last 2 weeks capture acute symptoms, creating a temporal mismatch that limits causal inference. As a result, the observed associations may partly reflect concurrent infectious etiologies or short-term environmental exposures rather than direct effects of long-term household fuel use.\u003c/p\u003e \u003cp\u003eAlso, the cross-sectional data and adjusted associations reported in our study for child, maternal, and household characteristics should not be interpreted as causal effects. These variables were included to control for confounding of the primary exposure and to describe patterns in symptom reporting rather than to identify independent causal risk factors. Therefore, all covariate results should be interpreted cautiously and descriptively. In addition, our study did not include potentially relevant variables, such as breastfeeding status, nutritional intake, immunization status, micronutrient supplementation, outdoor air pollution, epidemic periods, or season of symptom onset, all of which are known to influence ARIs.\u003c/p\u003e \u003cp\u003eFinally, DHS data may have a hierarchical structure; however, we did not account for this (for example, by clustering), which may affect the precision of our estimates. We do not have information on whether more than one child was included per household. Nevertheless, given that the analysis was restricted to children under 2 years old, it is less likely that multiple eligible children were present within the same household, and thus, potential intra-household correlation is expected to be limited.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings suggest that children under 2 years old living in households using unclean cooking fuels in both urban and rural areas are more likely to experience cough and dyspnea than those in urban, clean households. Beyond fuel type, our study identified several important risk factors associated with ARI-related symptoms being born smaller than average, and being the first, fifth, or later child of the mother. These results have direct policy relevance for advancing Sustainable Development Goal 7, universal access to clean energy, and suggest that interventions should prioritize expanding clean cooking fuels, improving household ventilation, and targeted caregiver education\u0026mdash;particularly in high-burden urban and rural areas. Together, these actions could meaningfully reduce household air pollution exposure and improve respiratory health outcomes for young children in East Africa and other similar settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003ePatients and Public Involvement Statement\u003c/h2\u003e \u003cp\u003eIt was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo any\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.N.Y. designed the project, participated in data analysis, and drafted and finalized the manuscript.T.B.R participated in merging and cleaning the dataset and reviewed and provided input on the manuscriptK.K.S. reviewed and provided input on the manuscriptA-M.H reviewed and provided feedback on the manuscript.A.C.C reviewed and provided input on the manuscriptM.A.K. supervised the final analysis and methodology approach, contributed to the interpretation of results, and finalized the manuscript.All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledge the Demographic and Health Surveys program for authorizing the use of their datasets and data collectors in each country. Most importantly we greatly appreciate study participants in each country for their time.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study is available from https://dhsprogram.com/data/DHS-Survey-Indicators-Characteristics-of-Households.cfm\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrganization WH. Pneumonia in children https://www.who.int/news-room/fact-sheets/detail/pneumonia2022 [\u003c/li\u003e\n\u003cli\u003eTekeba B, Gebrehana DA, Mekonnen EG, Zegeye AF, Mekonnen CK, Abate HK, et al. The comorbidities of diarrhea and acute respiratory tract infection and risk factors among under-five children in 45 low- and middle-income countries. Sci Rep. 2025;15(1):30139.\u003c/li\u003e\n\u003cli\u003eEric A. F. Simoes TC, Jeffrey Chow, Sonbol A. Shahid-Salles, Ramanan Laxminarayan, and T. Jacob John. Chapter 25Acute Respiratory Infections in Children. https://www.ncbi.nlm.nih.gov/books/NBK11786/2006.\u003c/li\u003e\n\u003cli\u003eOrganization. WH. Integrated global action plan for prevention and control of pneumonia and diarrhoea (GAPPD). . Geneva; 2013. .\u003c/li\u003e\n\u003cli\u003eZhu H, Huang K, Han X, Pan Z, Cheng H, Wang Q, et al. The burden of acute respiratory infection in children under 5 attributable to economic inequality in low- and middle-income countries. BMJ Glob Health. 2025;10(3).\u003c/li\u003e\n\u003cli\u003eChilot D, Shitu K, Gela YY, Getnet M, Mulat B, Diress M, et al. Factors associated with healthcare-seeking behavior for symptomatic acute respiratory infection among children in East Africa: a cross-sectional study. BMC Pediatr. 2022;22(1):662.\u003c/li\u003e\n\u003cli\u003eNair H, Simoes EA, Rudan I, Gessner BD, Azziz-Baumgartner E, Zhang JSF, et al. Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis. Lancet. 2013;381(9875):1380-90.\u003c/li\u003e\n\u003cli\u003eDherani M, Pope D, Mascarenhas M, Smith KR, Weber M, Bruce N. Indoor air pollution from unprocessed solid fuel use and pneumonia risk in children aged under five years: a systematic review and meta-analysis. Bull World Health Organ. 2008;86(5):390-8C.\u003c/li\u003e\n\u003cli\u003eAndualem Z, Nigussie Azene Z, Dessie A, Dagne H, Dagnew B. Acute respiratory infections among under-five children from households using biomass fuel in Ethiopia: systematic review and meta-analysis. Multidiscip Respir Med. 2020;15(1):710.\u003c/li\u003e\n\u003cli\u003eWHO. Defining clean fuels and technologies. https://www.who.int/tools/clean-household-energy-solutions-toolkit/module-7-defining-clean. 2025 [\u003c/li\u003e\n\u003cli\u003eSmith KR, Bruce N, Balakrishnan K, Adair-Rohani H, Balmes J, Chafe Z, et al. Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution. Annu Rev Public Health. 2014;35:185-206.\u003c/li\u003e\n\u003cli\u003eFandino-Del-Rio M, Kephart JL, Williams KN, Moulton LH, Steenland K, Checkley W, et al. Household air pollution exposure and associations with household characteristics among biomass cookstove users in Puno, Peru. Environ Res. 2020;191:110028.\u003c/li\u003e\n\u003cli\u003eHoffmann B, Boogaard H, de Nazelle A, Andersen ZJ, Abramson M, Brauer M, et al. WHO Air Quality Guidelines 2021-Aiming for Healthier Air for all: A Joint Statement by Medical, Public Health, Scientific Societies and Patient Representative Organisations. Int J Public Health. 2021;66:1604465.\u003c/li\u003e\n\u003cli\u003eKilbo Edlund K, Kisiel, M.A., Asker, C. et al. . High-resolution dispersion modelling of PM2.5, PM10, NOx and NO2 exposure in metropolitan areas in Sweden 2000‒2018 \u0026ndash; large health gains due to decreased population exposure. Air Qual Atmos Health 17. 2024;17:1661\u0026ndash;75 \u003c/li\u003e\n\u003cli\u003eTondel M, Kisiel MA, Barregard L, Dahlquist M, Edlund KK, Eriksson C, et al. Metabolic syndrome in the SCAPIS cohort - Investigating associations at low level exposure to ambient air pollution. Sci Total Environ. 2025;995:180120.\u003c/li\u003e\n\u003cli\u003eTrachsel D, Erb TO, Hammer J, von Ungern-Sternberg BS. Developmental respiratory physiology. Paediatr Anaesth. 2022;32(2):108-17.\u003c/li\u003e\n\u003cli\u003eAfrica. EAiEaS. World Bank https://www.worldbank.org/en/region/afr/brief/afe-energy. 2025 [\u003c/li\u003e\n\u003cli\u003eMidulla EEF. ERS Handbook of Paediatric Respiratory Medicine. European Respiratory Sociaty2021.\u003c/li\u003e\n\u003cli\u003eFenta HM, Zewotir TT, Naidoo S, Naidoo RN, Mwambi H. Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches. Sci Rep. 2024;14(1):15801.\u003c/li\u003e\n\u003cli\u003eAremu O, Aremu OO. Effect of Household Air Pollution and Neighbourhood Deprivation on the Risk of Acute Respiratory Infection Among Under-Five Children in Chad: A Multilevel Analysis. Int J Environ Res Public Health. 2025;22(5).\u003c/li\u003e\n\u003cli\u003eAmadu I, Seidu AA, Mohammed A, Duku E, Miyittah MK, Ameyaw EK, et al. Assessing the combined effect of household cooking fuel and urbanicity on acute respiratory symptoms among under-five years in sub-Saharan Africa. Heliyon. 2023;9(6):e16546.\u003c/li\u003e\n\u003cli\u003eICF. International. Demographic and Health Survey Sampling and Household Listing Manual. . 2012.\u003c/li\u003e\n\u003cli\u003eDHS. Guide to DHS Statistics. https://www.dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm. \u003c/li\u003e\n\u003cli\u003eWest KP, Sznajder KK, Hwang G, Sauve HE, Roba KT, Baatiema L, et al. Unclean cooking fuel use and stillbirth in Ghana: evidence from the 2022 DHS. Front Glob Womens Health. 2025;6:1636924.\u003c/li\u003e\n\u003cli\u003eEmery K, Studer M, Berchtold A. Comparison of imputation methods for univariate categorical longitudinal data. Qual Quant. 2025;59(2):1767-91.\u003c/li\u003e\n\u003cli\u003eMickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1):125-37.\u003c/li\u003e\n\u003cli\u003eSarfo JO, Amoadu M, Gyan TB, Osman AG, Kordorwu PY, Adams AK, et al. Acute lower respiratory infections among children under five in Sub-Saharan Africa: a scoping review of prevalence and risk factors. BMC Pediatr. 2023;23(1):225.\u003c/li\u003e\n\u003cli\u003eKhalequzzaman M, Kamijima M, Sakai K, Ebara T, Hoque BA, Nakajima T. Indoor air pollution and health of children in biomass fuel-using households of Bangladesh: comparison between urban and rural areas. Environ Health Prev Med. 2011;16(6):375-83.\u003c/li\u003e\n\u003cli\u003eCarter E, Norris C, Dionisio KL, Balakrishnan K, Checkley W, Clark ML, et al. Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter. Environ Health Perspect. 2017;125(7):076002.\u003c/li\u003e\n\u003cli\u003eMasekela R, Vanker A. Lung Health in Children in Sub-Saharan Africa: Addressing the Need for Cleaner Air. Int J Environ Res Public Health. 2020;17(17).\u003c/li\u003e\n\u003cli\u003eMurray EL, Brondi L, Kleinbaum D, McGowan JE, Van Mels C, Brooks WA, et al. Cooking fuel type, household ventilation, and the risk of acute lower respiratory illness in urban Bangladeshi children: a longitudinal study. Indoor Air. 2012;22(2):132-9.\u003c/li\u003e\n\u003cli\u003eYitageasu G, Tesfaye AH, Worede EA, Kifle T, Tigabie M, Gizaw Z, et al. Determinants and spatial patterns of solid fuel use in East Africa based on demographic and health survey data from 2012 to 2023. Sci Rep. 2025;15(1):28142.\u003c/li\u003e\n\u003cli\u003eOtani K, Saito M, Okamoto M, Tamaki R, Saito-Obata M, Kamigaki T, et al. Incidence of lower respiratory tract infection and associated viruses in a birth cohort in the Philippines. BMC Infect Dis. 2022;22(1):313.\u003c/li\u003e\n\u003cli\u003eAmsalu ET, Akalu TY, Gelaye KA. Spatial distribution and determinants of acute respiratory infection among under-five children in Ethiopia: Ethiopian Demographic Health Survey 2016. PLoS One. 2019;14(4):e0215572.\u003c/li\u003e\n\u003cli\u003eRahman A, Hossain MM. Prevalence and determinants of fever, ARI and diarrhea among children aged 6-59 months in Bangladesh. BMC Pediatr. 2022;22(1):117.\u003c/li\u003e\n\u003cli\u003eFeng Z, Liao M, Zhang L. Sex differences in disease: sex chromosome and immunity. J Transl Med. 2024;22(1):1150.\u003c/li\u003e\n\u003cli\u003eBras H, Mandemakers J. Maternal education and sibling inequalities in child nutritional status in Ethiopia. SSM Popul Health. 2022;17:101041.\u003c/li\u003e\n\u003cli\u003eBali VA, \u0026amp; Guo, J. . Birth order and child health (IFAU Working Paper 2017:16). . Institute for Evaluation of Labour Market and Education Policy (IFAU). https://www.ifau.se/globalassets/pdf/se/2017/wp2017-16-birth_order_and_child_health.pdf; 2017.\u003c/li\u003e\n\u003cli\u003eGeremew BM, Gelaye KA, Melesse AW, Akalu TY, Baraki AG. Factors Affecting Under-Five Mortality in Ethiopia: A Multilevel Negative Binomial Model. Pediatric Health Med Ther. 2020;11:525-34.\u003c/li\u003e\n\u003cli\u003eAkinyemi JO, Banda P, De Wet N, Akosile AE, Odimegwu CO. Household relationships and healthcare seeking behaviour for common childhood illnesses in sub-Saharan Africa: a cross-national mixed effects analysis. BMC Health Serv Res. 2019;19(1):308.\u003c/li\u003e\n\u003cli\u003eAshira Menashe-Oren PB, Carren Ginsburg, Yacouba Compaore, Mark Collinson. The dynamic role of household structure on under-5 mortality in southern and eastern subSaharan Africa. https://www.demographic-research.org/volumes/vol49/11/49-11.pdf; 2023.\u003c/li\u003e\n\u003cli\u003eMerera AM. Determinants of acute respiratory infection among under-five children in rural Ethiopia. BMC Infect Dis. 2021;21(1):1203.\u003c/li\u003e\n\u003cli\u003eFatmi Z, White F. A comparison of \u0026apos;cough and cold\u0026apos; and pneumonia: risk factors for pneumonia in children under 5 years revisited. Int J Infect Dis. 2002;6(4):294-301.\u003c/li\u003e\n\u003cli\u003eDeJonge PM, Monto AS, Malosh RE, Petrie JG, Callear A, Segaloff HE, et al. Comparing the Etiology of Viral Acute Respiratory Illnesses Between Children Who Do and Do Not Attend Childcare. Pediatr Infect Dis J. 2023;42(6):443-8.\u003c/li\u003e\n\u003cli\u003eAhmed KY, Dadi AF, Kibret GD, Bizuayehu HM, Hassen TA, Amsalu E, et al. Population modifiable risk factors associated with under-5 acute respiratory tract infections and diarrhoea in 25 countries in sub-Saharan Africa (2014-2021): an analysis of data from demographic and health surveys. EClinicalMedicine. 2024;68:102444.\u003c/li\u003e\n\u003cli\u003eAsresie MB, Y.; Vicendese, D.; Batra, M.; Erbas, B. . The Effect of Maternal Antenatal Care Utilisation on Childhood Acute Respiratory Infection: A Systematic Review and Meta-Analysis. . Int J Environ Res Public Health 2025;22:1627.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory infection-related symptom, cough, dyspnea, children under 2 years old, urban and rural, unclean cooking fuel, East Africa, Demographic Health Survey","lastPublishedDoi":"10.21203/rs.3.rs-8920900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8920900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e\u003cbr\u003e\nLimited evidence exists on how urbanicity in LMICs and fuel type affect infant ARI risk. This study examined the prevalence of cough and dyspnea across ten East African countries and the associations of these symptoms with fuel type and residence among children aged under two years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nPooled Demographic and Health Survey data (2015–2023) from mother–child pairs across ten East African countries were analyzed. Outcomes included cough and dyspnea in children reported by the mother within the past 2 weeks. The exposure was a four-category of cooking fuel type and residence (urban-clean vs rural-clean, urban-unclean, rural-unclean). Multivariable logistic regression was adjusted for child, maternal, and household characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nThere were 38,849 infants. Cough prevalence ranged from 13 (Mozambique) to 44% (Uganda), and dyspnea from 3% (Tanzania) to 21% (Uganda). Most households (96%) used unclean fuels, and 79% were rural. Compared with urban–clean households, the adjusted odds of cough were higher in urban–unclean (aOR=1.36; 95% CI:1.06–1.74) and rural–unclean households (aOR=1.40; 95% CI:1.09–1.81). Dyspnea odds were higher in urban–unclean (aOR=2.21; 95% CI:1.46–3.74) and in rural–unclean households (aOR=3.08; 95% CI:1.89–5.01). Children aged 12–23 months had higher odds of cough (aOR=1.16; 95% CI: 1.09-1.22) than those 0–11 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003cbr\u003e\n Children under 2 in households using unclean fuel, whether rural or urban, experienced more cough and dyspnea than those using clean fuel. Promoting clean fuel and early health care could reduce ARI symptoms in LMICs.\u003c/p\u003e","manuscriptTitle":"Association Between Urbanicity–Cooking Fuel Type and Acute Respiratory Infection Symptoms Among Children Under Two Years across Ten East African countries.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 14:14:31","doi":"10.21203/rs.3.rs-8920900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-03T05:34:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T05:20:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T19:42:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69524479642077229366654516208331803327","date":"2026-04-18T07:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19577616200212908909556501889172280937","date":"2026-04-16T14:43:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127534852185500732369212774750991375881","date":"2026-04-13T13:03:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T20:28:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154122411681916550398285548431848088835","date":"2026-03-31T09:29:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95105298107779715077647446002352353237","date":"2026-03-30T11:34:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44136411532188676596526072846146140821","date":"2026-03-26T10:47:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79627609149405801857865075821527951258","date":"2026-03-16T04:03:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T13:57:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T00:01:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T14:40:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2026-02-25T08:15:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91de5320-246e-461e-8611-2c04c65234e0","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-03T05:34:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T05:20:21+00:00","index":477,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T10:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 14:14:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8920900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8920900","identity":"rs-8920900","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}