Assessing Biological Vulnerability of Acute Respiratory Tract Infection Among Children: Evidence from Bangladesh Demographic and Health Survey 2017-18

Assessing Biological Vulnerability of Acute Respiratory Tract Infection Among Children: Evidence from Bangladesh Demographic and Health Survey 2017-18 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Biological Vulnerability of Acute Respiratory Tract Infection Among Children: Evidence from Bangladesh Demographic and Health Survey 2017-18 Rashmi Rashmi, Ronak Paul This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-477731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Acute respiratory tract infections (ARIs) are the leading infectious disease worldwide and continues to be the single largest morbidity contributor in children. One of the most densely populated countries, Bangladesh also threatens by alarming under-five childhood morbidity, which has aggravated in past years with the COVID-19 pandemic. This study attempts to understand the biological factors affecting the pre-existing respiratory tract infections in under 5 children of Bangladesh. Methods: The present study uses data from 8398 children aged below 5 years during the survey from the Demographic and Health Survey of Bangladesh (BDHS 2017-18). Both bivariate and multivariate analyses were performed to understand the biological vulnerability factors of pre-existing acute respiratory tract infections (ARIs) in under five Bangladeshi children and relate them with the potential impact of the COVID-19 pandemic. Further, to show effectively the effect of different risk factors on child morbidity status, we have summarized all the results into prediction graphs at various levels of one variable as the other variable changes Results: Children aged one year were 1.40 [95% CI: 1.16, 1.67] and 2.01 [95% CI: 1.70, 2.36] times more likely to experience single morbidity and comorbidity respectively compared to children aged four years. We observe that male children were 1.18 [95% CI: 1.07, 1.31] times more likely to experience comorbidity compared to their female counterparts. Prediction graphs confirm the multivariate analysis as the probability of comorbidity remains higher in the monsoon season among children, with little change in the summer and winter seasons. Further, Rajshahi administrative division followed by Barisal and Rangpur shows the highest probability of comorbid condition in Bangladesh. Conclusion: Biological factors emerged as the prominent contributor in child ARIs condition. More care is required as the nationwide lockdown due to the COVID-19 pandemic had not only isolated the people from physical communication but also disrupted the health care facilities to care for the pre-existing morbidity condition among Bangladeshi children. Insightful strategies are required to prevent infectious diseases in children right from their homes by focusing on their biological vulnerabilities. Infectious Diseases Comorbidity Children under-five years Infectious diseases Respiratory Infection Biological vulnerability Vulnerability to COVID-19 Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background With the quick spread of the COVID-19 pandemic, health authorities had also prioritized the well-being of individuals especially the vulnerable section of society. While data from Italy shows that the elderly were more susceptible to fatality from this disease [ 1 ], studies also show that children were most likely to be spared from the direct mortality status through COVID-19 involvement [ 2 ]. However, the indirect effect of COVID-19 on children due to interruption in health care facilities and services was yet to be discovered. A study from 118 low- and middle-income countries by the Johns Hopkins Bloomberg School of Public Health shows that the indirect effect of reduction in health coverage services during a pandemic can be the reason for 2 million under-five deaths which in turn would be responsible for bringing back the decades of progress across the world [ 3 ]. One of the most densely populated countries worldwide, Bangladesh is also threatened by the indirect effect of COVID-19 on children’s health as they still face an alarming situation in under five childhood morbidity [ 4 ]. Diarrhoea and respiratory tract infections including fever, cough, and breathing problems are the common infectious diseases that are prevalent across Bangladeshi children [ 5 ]. In 2015, a systematic analysis projected the death of 4.4 million under 5 children due to infectious diseases like diarrhoea and respiratory tract infections in 2030 [ 6 ]. Acute Respiratory Tract Infection, caused by virus or bacteria, is classified as upper respiratory tract infections (URIs) (airways from nostrils to vocal cords including paranasal sinuses and middle ear) and lower respiratory tract infections (LRIs) (airways from trachea and bronchi to the bronchioles and the alveoli) [ 7 ]. In developing countries, ARIs remains to be the single largest illness as it is responsible for 70% of under-five morbidity condition [ 8 ]. ARIs are the common cause of illness and mortality among under 5 age children who suffer from an average of three to six episodes regardless of their living condition [ 9 ]. The situation can be worsened when the child experiences other conditions like fever and cough along with acute respiratory infection. As such comorbidity can lead to further extension of ARIs to infection, inflammation and reduced lung function [ 7 ]. Comorbidity is defined as the occurrence of more than one morbid condition in the same individual either at the same time or in some causal sequence [ 10 ]. Today, it is common to experience more than one morbid condition for an individual as the comorbid condition is not limited to any age group. The situation can be more precisely explained by introducing the term biological vulnerability as it defines a condition when an individual who is vulnerable to something is more likely to get affected by it [ 11 ]. For instance, children under 5 years of age are more biologically vulnerable to infectious diseases as they are more likely to get affected by such diseases. Further, to understand such a condition it is essential to consider the situation of comorbidity which can be the causal sequence of a disease. Studies have associated ARIs with the exposure, environment, and background of children [ 12 ]. Evidence also shows that the risk of acute respiratory infection was common in younger ages [ 13 , 14 ]. A study from Bangladesh had shown that smoking habits among family members, location of the kitchen, and type of cooking fuels play an important role in acute respiratory infection incidence [ 15 ]. Studies have also shown that urban and male children were lesser likely to experience these diseases due to a preference for better food and health care facilities [ 15 ]. Numerous factors like parental education, household income and living condition were found to be associated with ARIs [ 13 , 16 – 18 ]. According to UNICEF, approximately 33 million Bangladeshi children are residing in poor living conditions with deprivation from basic human needs like food, health, education and sanitation [ 19 ]. Studies have shown that the poor living condition of children is highly associated with their deteriorated health which can be further aggravated due to lack of access to improved water and sanitation sources [ 20 , 21 ]. In Bangladesh, water and sanitation insecurity had continued to be the largest challenge throughout the years as still, 60% of the population are lacking the proper accessibility which had further increased the burden of infectious disease like acute respiratory tract infection (ARIs) [ 22 ]. Despite numerous efforts to reduce the mortality condition in under-five children, Bangladesh continues to experience high ARI-related mortality of one in every five deaths [ 23 , 24 ]. Moreover, with COVID-19, immense pressure is expected on Bangladeshi children who are already facing health-related challenges. In this regard, it is reasonable to understand the current situation of pre-existing morbidity conditions (in terms of acute respiratory tract infections) of under-five Bangladeshi children. Recently released data of Demographic and Health Survey of Bangladesh (BDHS 2017-18) provides an opportunity to understand the morbidity condition of under-five children. To the best of our knowledge, this is the most recent data which provides national-level information from Bangladesh, before the outbreak of the COVID-19 pandemic. This study can provide insights for the planning of mitigation strategies to take care of under-five children during and after the COVID-19 pandemic. The present study aims to provide the biological vulnerability factors of pre-existing respiratory tract infection in under five Bangladeshi children. 2. Data, Variables And Methods 2.1 Data source The current study used the most recent Demographic and Health Survey of Bangladesh conducted during 2017-18 (hereby referred to as BDHS 2017-18). The National Institute for Population Research and Training (NIPORT) has conducted BDHS 2017-18 under the stewardship of the Ministry of Health and Family Welfare (MoHFW) of Bangladesh. The survey provided crucial information on information on childhood mortality levels, maternal and child health, fertility and fertility preferences, utilization of family planning methods, newborn care, women’s empowerment, selected non-communicable diseases (NCDS) and availability and accessibility of health and family planning services at the community level. The survey follows a two-stage stratified sample design. Further details regarding sample design, survey instruments, fieldwork and training of staff, data collection and processing, and response rates are available in the BDHS 2017-18 reports [ 24 ]. We used the data for 8759 children aged under-five years born to 7562 mothers aged 15–49 years in Bangladesh. However, we dropped the records of 361 children who were not alive during the survey period and had no information regarding their morbidity status. Therefore, the analytical sample for this study is 8398 children under five years of age. 2.3 Outcome variables The outcome variable of morbidity status comes from the mother’s responses regarding the knowledge of their children’s morbidity. BDHS 2017-18 collected information regarding whether the children had suffered from fever, cough, and acute respiratory infections (ARI) within two weeks before the survey. We combined these three variables into a single variable of morbidity status with contained three categories which are – children who did not suffer from any of the three morbidities (“no condition”); children who suffered from “single condition”; and children experiencing two and more conditions (“comorbidity”). The advantage of this approach is that it aloows us to take into account the severity of the children’s infirmity [ 5 ]. 2.4 Explanatory variables Guided by extant research, we identified relevant factors that are associated with the occurrence of morbidity among children [ 5 , 12 , 13 ]. Accordingly, we included relevant explanatory variables, conditional upon their availability in BDHS 2017-18. The child-related characteristics are – age in years (less than one, one, two, three, four) and gender (male, female). The parent-related characteristics are – mother’s level of education (no formal education, upto primary, secondary and above), father’s level of education (no formal education, upto primary, secondary and above). The household-level factors are – household sanitation condition (poor, average, good), household members drink treated water (no, yes), type of handwashing place (Private space, Public place, No handwashing place), shares toilet with other households (Not shared, Shared by two households (HH), Shared by three HH, Shared by four and more HH), wealth quintile (poorest, poor, middle, rich, richest), the religion of household head (Islam, Hinduism, others). Further, the season during the interview (Summer, Monsoon, Winter), place of residence (City Corporation, urban areas other than City Corporation, Rural areas), and administrative division (Dhaka, Chittagong, Barisal, Khulna, Mymensingh, Rajshahi, Rangpur, Sylhet) were also included. Taking a cue from extant research, the household sanitation condition variable was constructed from three variables – type of source of drinking water, type of sanitation facility and the number of members per room in the household [ 25 ]. Respondents were asked about the source of household drinking water and as per prevalent standards, we recoded the source of household drinking water into two categories – “unimproved” coded as 0 (consisting of “dug, open well”, “river”, “pond”, “truck” and “bottled” categories from the original variable) and “improved” coded as 1 (consisting of “piped”, “tube well”, “hand pump”, “covered well” and “rainwater” categories from the original variable) [ 26 ]. Similarly, we recoded the type of household toilet facility into – “unimproved” coded as 0 (consisting of “defecation in open fields” and “traditional pit latrine” categories from the original variable) and “improved” coded as 1 (consisting of “ventilated improved pit latrine” and “flush toilet” categories from the original variable) [ 26 ]. Similarly, households with less than 3 members per room were coded as “1” and those with 3 or more members were coded as “0”. After this, we added the three variables to obtain a household sanitation condition score. Households with a score of 3, score of 2 and score less than 2 were categorized as having “good”, “average” and “poor” sanitation condition respectively. To avoid multicollinearity, we coded a new wealth quintile variable after excluding information on household water source and toilet facility. The wealth quintile variable was prepared using standard procedures that are documented elsewhere [ 27 ]. 2.5 Statistical methods We performed bivariate and multivariate analysis to realize the study objectives. Owing to the categorical nature of the outcome variable, the bivariate association was examined using the chi-square test for association. Equivalently, multivariable analysis was performed by estimating multinomial regression models. The multivariate association of morbidity status of children with the explanatory variables was shown using relative risk ratios. Relative risk ratio gives the risk (multiple times) of having comorbidity (or single morbidity) compared to having no morbidity among those children belonging to a particular category of an explanatory variable given the effect of all the other explanatory variables remain constant [ 28 ]. To show effectively the effect of different risk factors on child morbidity status, we have summarized the regression output into graphs of predicted probability [ 29 ]. We checked for multicollinearity in the regression model and found the mean value of the variance inflation factor (VIF) to be less than 1.25. Therefore, multicollinearity is negligible [ 30 ]. Further, the Hausman-McFadden test revealed that our estimated model did not violate the independence from irrelevant alternatives (IIA) assumption [ 31 ]. All statistical estimations were performed using the STATA software version 13.0 [ 32 ]. 3. Results 3.1 Sample description Table-1 shows the characteristics of 8,398 children aged under five years during BDHS 2017-18. Nearly 21% of children were in the age group less than 1 year and 52% of children were male. Nearly, 7% and 15% of children had a mother and father with no schooling education. One in every ten children comes from a household with poor sanitation conditions and 89% of children are from households where drinking water is untreated. Public space handwashing was common (64%) and most of the children did not share toilets with their family members (67%). Nearly, 27% of the population belongs to the poorest wealth quintile households and 65% reside in rural areas. In terms of population numeric, Chittagong is the largest division (17%) followed by the Dhaka division (15%) which includes the country’s capital city Dhaka. Table 1 Absolute (N) and percentage (%) distribution of children under-five years by demographic, parent-related, household socio-economic and spatial covariates Characteristics Total population N % Age of child (in years) Four 1,694 20.2 Three 1,587 18.9 Two 1,655 19.7 One 1,666 19.8 Less than 1 year 1,796 21.4 Gender of child Female 4,027 48.0 Male 4,371 52.0 Mother's level of education Secondary and above 5,371 64.0 Upto primary 2,420 28.8 No formal education 607 7.2 Father's level of education Secondary and above 4,356 51.9 Upto primary 2,811 33.5 No formal education 1,231 14.7 Household sanitation condition Poor 931 11.1 Average 3,061 36.4 Good 4,406 52.5 Water treated before drinking Yes 926 11.0 No 7,472 89.0 Type of handwashing place Private space 2,726 32.5 Public space 5,374 64.0 No handwashing place 298 3.5 Shares Toilet with other households Not shared 5,624 67.0 Shared by 2 HH 1,311 15.6 Shared by 3 HH 690 8.2 Shared by 4 and more HH 773 9.2 Household wealth quintile Richest 1,641 19.5 Rich 1,646 19.6 Middle 1,438 17.1 Poor 1,400 16.7 Poorest 2,273 27.1 Religion of household head Islam 7,694 91.6 Hinduism 655 7.8 Others 49 0.6 Type of season Summer 662 7.9 Winter 7,233 86.1 Monsoon 503 6.0 Place of residence City corporation 776 9.2 Other urban areas 2,152 25.6 Rural areas 5,470 65.1 Country administrative division Dhaka 1,246 14.8 Chittagong 1,393 16.6 Barisal 863 10.3 Khulna 872 10.4 Mymensingh 991 11.8 Rajshahi 874 10.4 Rangpur 934 11.1 Sylhet 1,225 14.6 Overall 8,398 100 Table-2 provides the morbidity profile of under-five Bangladeshi children. We found that 54.5% of children had no morbidity within two weeks before the survey. In comparison, there were 18.4% of children in single morbid condition, co-morbidity was common among 27.1% of Bangladeshi children. Among all comorbid condition, children experiencing both fever and cough was high (15.1%). While only respiratory infection contributes to 0.5% of the population, however, the combined condition of respiratory infections accompanied by fever and cough increases the prevalence to 9% in the total population. Table 2 Morbidity profile of children under-five years Morbidity profile of children Population distribution Comorbidity status Population distribution N % N % No morbidity 4,574 54.5 No condition 4,574 54.5 Only fever 680 8.1 Single condition 1,545 18.4 Only cough 820 9.8 Only respiratory infection 45 0.5 Fever and Cough 1,270 15.1 Comorbidity 2,279 27.1 Cough and Respiratory infection 195 2.3 Fever and Respiratory infection 58 0.7 Fever, Cough and Respiratory infection 756 9.0 Overall 8,398 100 8,398 100 3.2 Bivariate analysis Table-3 shows the bivariate association between morbidity incidence and the explanatory variables. Morbidity condition was higher in children aged one year than those with other age categories (Single morbidity: 20.2%; Comorbidity: 33%). Approximately 29% of male children experienced comorbidity compared to 26% in females. Nearly 27.7% of children who drink untreated water experienced comorbidity compared to 23% of children who drink treated water. The incidence of comorbidity was higher if households do not practice handwashing (30.9%) in comparison to those households which used private (27.4%) and public space for handwashing (26.8%). Moreover, the incidence of comorbidity was higher during the monsoon season and in rural areas. Further, comorbidity incidence was higher (more than 31%) in the Barisal, Rajshahi, and Rangpur divisions. The association of morbidity status with stunting status, father’s education level, and household sanitation condition were not statistically significant. Table 3 Bivariate association between morbidity incidence and the demographic, parent-related, household socio-economic and spatial covariates Characteristics Total Comorbidity status ꭓ2 test of association population No condition Single condition Comorbidity N N % N % N % Age of child (in years) Four 1,694 1,026 60.6 312 18.4 356 21.0 ꭓ2 = 99.94; p-value = 0.001 Three 1,587 942 59.4 262 16.5 383 24.1 Two 1,655 896 54.1 307 18.5 452 27.3 One 1,666 780 46.8 336 20.2 550 33.0 Less than 1 year 1,796 930 51.8 328 18.3 538 30.0 Gender of child Female 4,027 2,260 56.1 739 18.4 1,028 25.5 ꭓ2 = 11.29; p-value = 0.004 Male 4,371 2,314 52.9 806 18.4 1,251 28.6 Mother's level of education Secondary and above 5,371 2,884 53.7 1,048 19.5 1,439 26.8 ꭓ2 = 14.97; p-value = 0.005 Upto primary 2,420 1,334 55.1 401 16.6 685 28.3 No formal education 607 356 58.6 96 15.8 155 25.5 Father's level of education Secondary and above 4,356 2,381 54.7 806 18.5 1,169 26.8 ꭓ2 = 3.44; p-value = 0.488 Upto primary 2,811 1,507 53.6 533 19.0 771 27.4 No formal education 1,231 686 55.7 206 16.7 339 27.5 Household sanitation condition Poor 931 544 58.4 157 16.9 230 24.7 ꭓ2 = 8.90; p-value = 0.064 Average 3,061 1,639 53.5 555 18.1 867 28.3 Good 4,406 2,391 54.3 833 18.9 1,182 26.8 Water treated before drinking Yes 926 570 61.6 148 16.0 208 22.5 ꭓ2 = 21.29; p-value = 0.001 No 7,472 4,004 53.6 1,397 18.7 2,071 27.7 Type of handwashing place Private space 2,726 1,474 54.1 504 18.5 748 27.4 ꭓ2 = 2.66; p-value = 0.615 Public space 5,374 2,946 54.8 989 18.4 1,439 26.8 No handwashing place 298 154 51.7 52 17.4 92 30.9 Shares Toilet with other households Not shared 5,624 3,092 55.0 1,023 18.2 1,509 26.8 ꭓ2 = 9.48; p-value = 0.148 Shared by 2 HH 1,311 680 51.9 251 19.1 380 29.0 Shared by 3 HH 690 357 51.7 135 19.6 198 28.7 Shared by 4 and more HH 773 445 57.6 136 17.6 192 24.8 Household wealth quintile Richest 1,641 955 58.2 303 18.5 383 23.3 ꭓ2 = 23.18; p-value = 0.003 Rich 1,646 875 53.2 302 18.3 469 28.5 Middle 1,438 770 53.5 269 18.7 399 27.7 Poor 1,400 761 54.4 277 19.8 362 25.9 Poorest 2,273 1,213 53.4 394 17.3 666 29.3 Religion of household head Islam 7,694 4,161 54.1 1,424 18.5 2,109 27.4 ꭓ2 = 13.55; p-value = 0.009 Hinduism 655 375 57.3 115 17.6 165 25.2 Others 49 38 77.6 6 12.2 5 10.2 Type of season Summer 662 386 58.3 113 17.1 163 24.6 ꭓ2 = 7.07; p-value = 0.132 Winter 7,233 3,933 54.4 1,335 18.5 27.2 Monsoon 503 255 50.7 97 19.3 151 30.0 Place of residence City corporation 776 495 63.8 140 18.0 141 18.2 ꭓ2 = 39.06; p-value = 0.001 Other urban areas 2,152 1,150 53.4 399 18.5 603 28.0 Rural areas 5,470 2,929 53.5 1,006 18.4 1,535 28.1 Country administrative division Dhaka 1,246 722 57.9 229 18.4 295 23.7 ꭓ2 = 57.30; p-value = 0.001 Chittagong 1,393 800 57.4 230 16.5 363 26.1 Barisal 863 445 51.6 161 18.7 257 29.8 Khulna 872 471 54.0 202 23.2 199 22.8 Mymensingh 991 524 52.9 195 19.7 272 27.4 Rajshahi 874 434 49.7 174 19.9 266 30.4 Rangpur 934 482 51.6 166 17.8 286 30.6 Sylhet 1,225 696 56.8 188 15.3 341 27.8 Overall 8,398 4,574 54.5 1,545 18.4 2,279 27.1 Note – (a) Morbidity status of children categorized into No condition, Single condition, and Comorbidity 3.2 Multivariate analysis Table-4 gives the multivariate association of morbidity status and the explanatory variables. Children aged one year were 1.40 [95% CI: 1.16, 1.67] and 2.01 [95% CI: 1.70, 2.36] times more likely to experience single morbidity and comorbidity respectively compared to children aged four years. We observe that male children were 1.18 [95% CI: 1.07, 1.31] times more likely to experience comorbidity compared to their female counterparts. Mothers with primary education show a lower chance of single morbid condition in their children [OR: 0.82; 95% CI: 0.71, 0.95]. Households with average sanitation conditions experience a higher probability of comorbidity than those with poor sanitation conditions [OR: 1.28; 95% CI: 1.07, 1.54]. Further, it is observed that children have a 1.64 [95% CI: 1.20, 2.26] times higher likelihood of comorbidity during the monsoon season in comparison to the summer season. Furthermore, children residing in rural areas and urban areas than city corporation involved 1.55 [95% CI: 1.22, 1.98] and 1.58 [95% CI: 1.25, 2.01] times greater risk of comorbidity respectively compared to living in areas under city corporation. In the multivariate analysis, the association of morbidity status with the father's education, religion and the facility of shared toilets loses its statistical significance. While observing the administrative division, Rajshahi [OR: 1.60; 95% CI: 1.24, 2.06] shows the higher likelihood of comorbidity followed by Barisal [OR: 1.53; 95% CI: 1.18, 2.00] and Rangpur [OR: 1.51; 95% CI: 1.17, 1.95] as compared to Dhaka. Table 4 Relative risk ratio showing the multivariate association between morbidity incidence and the demographic, parent-related, household socio-economic and spatial covariates Characteristics Morbidity Status Single condition Comorbidity RRR 95% CI RRR 95% CI Age of child (in years) Four® Three 0.91 (0.76–1.10) 1.16 (0.98–1.38) Two 1.12 (0.93–1.34) 1.43* (1.21–1.69) One 1.40* (1.16–1.67) 2.01* (1.70–2.36) Less than 1 year 1.14 (0.95–1.36) 1.64* (1.39–1.93) Gender of child Female® Male 1.06 (0.95–1.20) 1.18* (1.07–1.31) Mother's level of education Secondary and above® Upto primary 0.82* (0.71–0.95) 1.02 (0.90–1.16) No formal education 0.78 (0.60–1.01) 0.91 (0.73–1.14) Father's level of education Secondary and above® Upto primary 1.11 (0.96–1.28) 1.00 (0.88–1.14) No formal education 1.02 (0.83–1.25) 1.01 (0.85–1.20) Household sanitation condition Poor® Average 1.17 (0.95–1.44) 1.28* (1.07–1.54) Good 1.20 (0.96–1.48) 1.22* (1.01–1.48) Water treated before drinking Yes® No 1.21 (0.97–1.51) 1.05 (0.86–1.27) Type of handwashing place Private space® Public space 0.94 (0.81–1.09) 0.84* (0.74–0.96) No handwashing place 1.08 (0.75–1.53) 1.03 (0.76–1.38) Shares Toilet with other households Not shared® Shared by 2 HH 1.09 (0.92–1.28) 1.13 (0.98–1.31) Shared by 3 HH 1.15 (0.92–1.42) 1.17 (0.97–1.42) Shared by 4 and more HH 0.96 (0.77–1.20) 0.97 (0.80–1.19) Household wealth quintile Richest® Rich 1.07 (0.88–1.31) 1.27* (1.06–1.52) Middle 1.07 (0.85–1.34) 1.18 (0.96–1.44) Poor 1.17 (0.92–1.49) 1.10 (0.88–1.36) Poorest 1.06 (0.84–1.35) 1.25* (1.02–1.54) Religion of household head Islam® Hinduism 0.91 (0.73–1.14) 0.89 (0.73–1.09) Others 0.52 (0.22–1.25) 0.31* (0.12–0.80) Type of season Summer® Winter 1.14 (0.89–1.45) 1.09 (0.88–1.35) Monsoon 1.36 (0.95–1.94) 1.64* (1.20–2.26) Place of residence City corporation® Other urban areas 1.03 (0.80–1.33) 1.58* (1.25–2.01) Rural areas 1.02 (0.79–1.32) 1.55* (1.22–1.98) Country administrative division Dhaka® Chittagong 0.91 (0.71–1.18) 1.27* (1.00–1.61) Barisal 1.12 (0.84–1.50) 1.53* (1.18–2.00) Khulna 1.35* (1.03–1.77) 1.19 (0.91–1.54) Mymensingh 1.20 (0.92–1.56) 1.33* (1.04–1.70) Rajshahi 1.21 (0.92–1.60) 1.60* (1.24–2.06) Rangpur 1.05 (0.79–1.40) 1.51* (1.17–1.95) Sylhet 0.91 (0.70–1.19) 1.36* (1.08–1.73) Number of children 8,398 8,398 Note – (a) RRR: relative risk ratio; (b) 95% Confidence Interval (CI) is given in brackets; (c) Statistical significance is denoted by asterisks where * denotes p-value < 0.05; (d) ® denotes reference category; (e) Morbidity status of children categorized into: no condition, single condition, comorbidity Results of the multivariate analysis were further confirmed by showing the predicted probability graphs for the factors having a statistically significant association with morbidity status. Figure-1 shows that the probability of acquiring single or comorbid (or multiple morbid) conditions increases with the decreasing age. Here, the highest single or comorbid condition is seen in the child’s first year of life. The probability of acquiring comorbidity condition among male children is higher, while little change in single morbidity condition is noticed across both genders (Figure-2). Figure-3 shows that the probability of comorbidity increases in the monsoon season among children, with little change in the summer and winter seasons. Children residing in other urban areas show a higher probability of comorbid conditions (figure-4). While observing the administrative division of Bangladesh, a scattered picture is noticed. The probability of comorbidity condition was higher at the Rajshahi division (figure-5). 4. Discussion Although children are not the face of the COVID-19 pandemic, the impact of this universal crisis can be lifelong for them [ 2 ]. COVID-19 mitigation strategies have usually enforced social distancing and isolation measures. However, such strategies may sometimes disrupt life-saving health services. Studies have shown that a sudden outbreak of pandemic had affected regular health care facilities and services [ 33 ]. In Bangladesh, acute respiratory infections (ARIs) are the most common morbidity condition which needs proper attention throughout the year. So, keeping in view the COVID-19 situation, the present study uses recent national-level data of Bangladesh to highlight the vulnerability factors of under-five childhood morbidity. We found a strong effect of age on the incidence of ARIs along with fever and cough and these results are consistent with the previous Bangladesh studies [ 5 ]. It has been usually found that children at their younger ages can get exposed to contaminated water, soil, and food easily as at these ages they usually crawl and tries to explore the environment. However, older ages children who have already moved towards this exposure are well-versed with their environment and sometimes build a strong immunity till that age. Incidence of comorbidity condition among under-five children also varies according to household wealth index in both single and multiple morbidity conditions. Multiple morbidities were found to be significantly higher among monsoon seasons which is consistent with a previous study showing the health impact of climate change [ 34 ]. However, in contrast to a previous Bangladesh study, the present study shows that comorbidity condition is higher among male children than females [ 35 ]. Rajshahi administrative division followed by Barisal and Rangpur shows the highest probability of comorbid condition. This may be due to the higher indigenous population in this area. Also, a WHO report has shown that throughout the decade, poverty in few administrative divisions like Rangpur had increased facing a weak health care system [ 36 ]. Results from predicted probability also confirm the pre-existing demographic risk factors of under-five childhood morbidity in Bangladesh. As the age and sex of the child, place of residence and administrative division of Bangladesh emerged as the detrimental factor for under-five morbidity. Water and sanitation condition (like sharing toilets with more people in a household) doesn’t affect significantly the morbidity status. These findings are consistent with a Bangladesh study where improved water and sanitation sources were not found to be significantly associated with childhood morbidity [ 37 ]. Further, another study had also provided evidence that water, sanitation and handwashing interventions did not affect the linear growth of children in Bangladesh [ 18 ]. This might be due to the reason that most of the Bangladeshi population lack proper access to improved water and sanitation sources. And the combined effect of both water and sanitation interventions should be considered for bringing favourable changes [ 37 ]. Also, there is the necessity to consider the other unobserved factors which may play role in comorbid conditions. The current pandemic can even worsen the situation of food insecurity, poverty, hunger, and malnutrition across the world. Previous studies have also shown the impact of the pandemic on the mental health of children [ 38 ]. Although the government had taken different measures to protect the well-being of children, the pandemic had increased the existing inequities and burdened the country with the risk of childhood disease or death. So, the unprecedented situation of COVID-19 draws our attention towards strengthening the public care facilities and identify the vulnerability factors which lead to the morbidity condition of children. The present study is also backed with the recent national-level data of under-five children in Bangladesh which will help us to evaluate the situation just before the pandemic. Our study will also help policymakers to explore different mitigation strategies. However, our study has some limitations too. First, our study provides only a cross-sectional view of the scenario and therefore does not allow us to examine causality. Second, the morbidity incidence was evaluated from the self-reported information provided by women. However, the short recall period of morbidity (two weeks before the survey) makes the chances of recall bias minimal. Also, there is a need to consider the unobserved factors which affect the association. 5. Conclusion The nationwide lockdown due to the COVID-19 pandemic had not only isolated the people from physical communication but also disrupted the health care facilities critical for mitigating the pre-existing morbidity condition among Bangladeshi children. During the pandemic, it was found that the access to regular health care services and continuity of care become worsened. Our study urges a greater investment by the government to mitigate the adverse impact of the pandemic and to enhance the programs which can reduce the effect of vulnerability factors. Although some of our findings suggest that individual intervention of water and sanitation may have no big advantages, both individual and combined investments are required according to delivering convenience. This may be promoted by sensitisation of individuals about insightful strategies to prevent infectious diseases in children right from their homes by focusing on their biological vulnerabilities. 6. List Of Abbreviations ARIs: Acute Respiratory Tract Infections NCDs: Non-communicable Diseases BDHS: Bangladesh Demographic Health Survey COVID-19: Coronavirus Disease-2019 UNICEF: United Nations International Children’s Emergency Fund NIPORT: National Institute for Population Research and Training MoHFW: Ministry of Health and Family Welfare 7. Disclosure Statements Ethics approval and consent to participate: The data is freely available in the public domain and survey agencies that conducted the field survey for the data collection have collected prior consent from the respondent. The local ethics committee of the International Institute for Population Sciences (IIPS), Mumbai, ruled that no formal ethics approval was required to research this data source. Consent for publication: Not applicable Availability of data and materials: The study uses a secondary source of data that is freely available in the public domain through: https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2017.cfm?flag=0 Competing Interests: The authors declare that they have no competing interests. Funding: Authors did not receive any funding to carry out this research. Author’s Contribution: The concept was drafted by RR; RP contributed to the analysis design, RP and RR advised on the paper and assisted in paper conceptualization. RP and RR contributed to the comprehensive writing of the article. All authors read and approved the final manuscript. Acknowledgements: We are thankful to Dr Hemkothang Lhungdim and Dr Harihar Sahoo of the International Institute for Population Sciences (IIPS) for their insightful comments and suggestion on an earlier version of this paper which was presented at the IIPS Seminar 2021. 8. References Rate C-F. Characteristics of Patients Dying in Relation to COVID-19 in Italy Onder G, Rezza G, Brusaferro S. JAMA Published online March. 2020;23. UN. UN Policy Brief: The Impact of COVID‐19 on children. 2020. Roberton T, Carter ED, Chou VB, Stegmuller AR, Jackson BD, Tam Y, et al. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study. The Lancet Global Health. 2020;8:e901–8. Balabanova D, Mills A, Conteh L, Akkazieva B, Banteyerga H, Dash U, et al. Good Health at Low Cost 25 years on: lessons for the future of health systems strengthening. The Lancet. 2013;381:2118–33. Kamal MM, Hasan MM, Davey R. Determinants of childhood morbidity in Bangladesh: evidence from the demographic and health survey 2011. BMJ open. 2015;5:e007538. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals. The Lancet. 2016;388:3027–35. Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al. Disease control priorities in developing countries. The World Bank; 2006. Selvaraj K, Chinnakali P, Majumdar A, Krishnan IS. Acute respiratory infections among under-5 children in India: A situational analysis. Journal of natural science, biology, and medicine. 2014;5:15. Monto AS, Ullman BM. Acute respiratory illness in an American community: the Tecumseh study. Jama. 1974;227:164–9. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. The Annals of Family Medicine. 2009;7:357–63. Hazelden F. The Stress-Vulnerability Model | Behavioral Health Evolution. 2016. https://www.bhevolution.org/public/stress-vulnerability.page. Black RE. Diarrheal diseases and child morbidity and mortality. Population and Development Review. 1984;10:141–61. Richardson A. Factors influencing acute respiratory infection of children in Bangladesh. International Journal of Statistics and Systems. 2013;8:239–50. Ferdous F, Das SK, Ahmed S, Farzana FD, Malek MA, Das J, et al. Diarrhoea in slum children: observation from a large diarrhoeal disease hospital in D haka, B angladesh. Tropical Medicine & International Health. 2014;19:1170–6. Azad SMY, Bahauddin KM, Uddin MH, Parveen S. Indoor air pollution and prevalence of acute respiratory infection among children in rural area of Bangladesh. Indoor Air. 2014;4. Azad KMAK. Risk factors for acute respiratory infections (ARI) among under-five children in Bangladesh. Journal of Scientific Research. 2009;1:72–81. Pinzón-Rondón ÁM, Aguilera-Otalvaro P, Zárate-Ardila C, Hoyos-Martínez A. Acute respiratory infection in children from developing nations: a multi-level study. Paediatrics and international child health. 2016;:1–7. Luby SP, Rahman M, Arnold BF, Unicomb L, Ashraf S, Winch PJ, et al. Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Bangladesh: a cluster randomised controlled trial. The Lancet Global Health. 2018;6:e302–15. UNICEF. 33 million children in Bangladesh live in poverty. 2009. 2009. https://www.unicef.org/media/media_51925.html. Rahman M, Ashraf S, Unicomb L, Mainuddin AKM, Parvez SM, Begum F, et al. WASH Benefits Bangladesh trial: system for monitoring coverage and quality in an efficacy trial. Trials. 2018;19:1–8. Tofail F, Fernald LCH, Das KK, Rahman M, Ahmed T, Jannat KK, et al. Effect of water quality, sanitation, hand washing, and nutritional interventions on child development in rural Bangladesh (WASH Benefits Bangladesh): a cluster-randomised controlled trial. The Lancet Child & Adolescent Health. 2018;2:255–68. Hedrick S. Water In Crisis - Spotlight Banglasdesh. The Water Project. 2016. https://thewaterproject.org/water-crisis/water-in-crisis-bangladesh. Halder AK, Gurley ES, Naheed A, Saha SK, Brooks WA, El Arifeen S, et al. Causes of early childhood deaths in urban Dhaka, Bangladesh. PLoS One. 2009;4:e8145. NIPORT, Ministry of Health and Family Welfare & I. Bangladesh Demographic and Health Survey, 2017-18. NIPORT; 2020. Paul R, Singh A. Does early childhood adversities affect physical, cognitive and language development in indian children? Evidence from a panel study. SSM-Population Health. 2020;12:100693. WHO-UNICEF. MEETING THE MDG DRINKING WATER SANITATION TARGET A N D A Mid-Term Assessment of Progress. 2004. Rutstein SO, Johnson K. The DHS wealth index. DHS comparative reports no. 6. Calverton: ORC Macro. 2004. Cameron AC, Trivedi PK. Microeconometrics: Methods and Applications . Cambridge University Press. 2005. http://cameron.econ.ucdavis.edu/mmabook/mma.html. Accessed 23 Jun 2020. Long JS, Freese J. Regression models for categorical dependent variables using Stata. Stata press; 2006. Ender P. collin”: Stata command to compute collinearity diagnostics. 2010. Cheng S, Long JS. Testing for IIA in the multinomial logit model. Sociological methods & research. 2007;35:583–600. StataCorp LP. Stata 13. College Station: StataCorp LP. 2013. Cash R, Patel V. Has COVID-19 subverted global health? The Lancet. 2020;395:1687–8. Khan AE, Xun WW, Ahsan H, Vineis P. Climate change, sea-level rise, & health impacts in Bangladesh. Environment: Science and Policy for Sustainable Development. 2011;53:18–33. Chen LC, Huq E, d’Souza S. Sex bias in the family allocation of food and health care in rural Bangladesh. Population and development review. 1981;:55–70. World Bank Group. Bangladesh Poverty Assessment Facing old and new frontiers in poverty reduction. 2019. Begum S, Ahmed M, Sen B. Do water and sanitation interventions reduce childhood diarrhoea? New evidence from Bangladesh. The Bangladesh Development Studies. 2011;:1–30. Yeasmin S, Banik R, Hossain S, Hossain MN, Mahumud R, Salma N, et al. Impact of COVID-19 pandemic on the mental health of children in Bangladesh: A cross-sectional study. Children and youth services review. 2020;117:105277. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-477731","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":25299838,"identity":"0d901186-8c2b-4a87-8a27-330a0ee2224d","order_by":0,"name":"Rashmi Rashmi","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rashmi","middleName":"","lastName":"Rashmi","suffix":""},{"id":25299839,"identity":"9e23b45d-d882-44d2-b14a-242f98a08875","order_by":1,"name":"Ronak Paul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBCDBCBmfAAkePhI0cJsANLCRooWNgkQi6AW+ejDBx8XVNzJ45fuPVb5NcdOho2B+eGjG3i0GJ5LSzaeceZZseScc2m3ZbclAx3GZmycg09LD4+ZNG/b4cQNN3LMbktuYwZq4WGTxq+F//tvkJb9QC3FktvqCWuR5+FhYwbbIpFjxvhx22HCWgx42Iylec48S5xxI8dYmnHbcaAJBPwi38P88DNPxZ3E/hk5hh9/bqu252dvfvgYry0HwBSEZOYBk3iUg21pQNLC+IOA6lEwCkbBKBiZAABVl0X1nZVm2wAAAABJRU5ErkJggg==","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ronak","middleName":"","lastName":"Paul","suffix":""}],"badges":[],"createdAt":"2021-04-29 12:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-477731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-477731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8913081,"identity":"2de92b37-bbe8-4301-ac1d-fbb82f42e067","added_by":"auto","created_at":"2021-05-07 13:51:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34865,"visible":true,"origin":"","legend":"Adjusted Predicted probability of Morbidity by age from the multinomial regression model","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/82cce78aeb12d4132c6e5366.jpg"},{"id":8912742,"identity":"e93a6f32-a4a9-4b6f-a17d-52c2fb0877c3","added_by":"auto","created_at":"2021-05-07 13:48:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31380,"visible":true,"origin":"","legend":"Adjusted Predicted probability of Morbidity by gender from the multinomial regression model","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/54ee0470a99a53e841faff3f.jpg"},{"id":8912391,"identity":"4ec517bc-5788-40f7-87d5-7357feb071c6","added_by":"auto","created_at":"2021-05-07 13:45:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30809,"visible":true,"origin":"","legend":"Adjusted Predicted probability of Morbidity by Type of season from the multinomial regression model","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/481409af9ea8b092c74c4231.jpg"},{"id":8912744,"identity":"b9cb75d2-9172-49d8-8fe1-2d1754f18d8f","added_by":"auto","created_at":"2021-05-07 13:48:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34265,"visible":true,"origin":"","legend":"Adjusted Predicted probability of Morbidity by Place of residence from the multinomial regression model","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/d71d490a47a320f23aef9b54.jpg"},{"id":8912745,"identity":"e2d55388-568a-41f9-849f-a8fff22d26d2","added_by":"auto","created_at":"2021-05-07 13:48:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35243,"visible":true,"origin":"","legend":"Adjusted Predicted probability of Morbidity by Country Administrative Region from the multinomial regression model","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/839d9625cd337d36e0b2694c.jpg"},{"id":13691220,"identity":"fc7d1f14-30c7-4526-b019-8ddcfc6034b3","added_by":"auto","created_at":"2021-09-17 12:37:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1119653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-477731/v1/1830104f-66a2-4b96-96aa-a1f79646e319.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Biological Vulnerability of Acute Respiratory Tract Infection Among Children: Evidence from Bangladesh Demographic and Health Survey 2017-18","fulltext":[{"header":"1. Background","content":" \u003cp\u003eWith the quick spread of the COVID-19 pandemic, health authorities had also prioritized the well-being of individuals especially the vulnerable section of society. While data from Italy shows that the elderly were more susceptible to fatality from this disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], studies also show that children were most likely to be spared from the direct mortality status through COVID-19 involvement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the indirect effect of COVID-19 on children due to interruption in health care facilities and services was yet to be discovered. A study from 118 low- and middle-income countries by the Johns Hopkins Bloomberg School of Public Health shows that the indirect effect of reduction in health coverage services during a pandemic can be the reason for 2\u0026nbsp;million under-five deaths which in turn would be responsible for bringing back the decades of progress across the world [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. One of the most densely populated countries worldwide, Bangladesh is also threatened by the indirect effect of COVID-19 on children\u0026rsquo;s health as they still face an alarming situation in under five childhood morbidity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiarrhoea and respiratory tract infections including fever, cough, and breathing problems are the common infectious diseases that are prevalent across Bangladeshi children [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In 2015, a systematic analysis projected the death of 4.4\u0026nbsp;million under 5 children due to infectious diseases like diarrhoea and respiratory tract infections in 2030 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Acute Respiratory Tract Infection, caused by virus or bacteria, is classified as upper respiratory tract infections (URIs) (airways from nostrils to vocal cords including paranasal sinuses and middle ear) and lower respiratory tract infections (LRIs) (airways from trachea and bronchi to the bronchioles and the alveoli) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In developing countries, ARIs remains to be the single largest illness as it is responsible for 70% of under-five morbidity condition [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. ARIs are the common cause of illness and mortality among under 5 age children who suffer from an average of three to six episodes regardless of their living condition [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The situation can be worsened when the child experiences other conditions like fever and cough along with acute respiratory infection. As such comorbidity can lead to further extension of ARIs to infection, inflammation and reduced lung function [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Comorbidity is defined as the occurrence of more than one morbid condition in the same individual either at the same time or in some causal sequence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Today, it is common to experience more than one morbid condition for an individual as the comorbid condition is not limited to any age group. The situation can be more precisely explained by introducing the term biological vulnerability as it defines a condition when an individual who is vulnerable to something is more likely to get affected by it [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For instance, children under 5 years of age are more biologically vulnerable to infectious diseases as they are more likely to get affected by such diseases. Further, to understand such a condition it is essential to consider the situation of comorbidity which can be the causal sequence of a disease.\u003c/p\u003e \u003cp\u003eStudies have associated ARIs with the exposure, environment, and background of children [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Evidence also shows that the risk of acute respiratory infection was common in younger ages [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A study from Bangladesh had shown that smoking habits among family members, location of the kitchen, and type of cooking fuels play an important role in acute respiratory infection incidence [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have also shown that urban and male children were lesser likely to experience these diseases due to a preference for better food and health care facilities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Numerous factors like parental education, household income and living condition were found to be associated with ARIs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. According to UNICEF, approximately 33\u0026nbsp;million Bangladeshi children are residing in poor living conditions with deprivation from basic human needs like food, health, education and sanitation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies have shown that the poor living condition of children is highly associated with their deteriorated health which can be further aggravated due to lack of access to improved water and sanitation sources [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Bangladesh, water and sanitation insecurity had continued to be the largest challenge throughout the years as still, 60% of the population are lacking the proper accessibility which had further increased the burden of infectious disease like acute respiratory tract infection (ARIs) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite numerous efforts to reduce the mortality condition in under-five children, Bangladesh continues to experience high ARI-related mortality of one in every five deaths [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, with COVID-19, immense pressure is expected on Bangladeshi children who are already facing health-related challenges. In this regard, it is reasonable to understand the current situation of pre-existing morbidity conditions (in terms of acute respiratory tract infections) of under-five Bangladeshi children. Recently released data of Demographic and Health Survey of Bangladesh (BDHS 2017-18) provides an opportunity to understand the morbidity condition of under-five children. To the best of our knowledge, this is the most recent data which provides national-level information from Bangladesh, before the outbreak of the COVID-19 pandemic. This study can provide insights for the planning of mitigation strategies to take care of under-five children during and after the COVID-19 pandemic. The present study aims to provide the biological vulnerability factors of pre-existing respiratory tract infection in under five Bangladeshi children.\u003c/p\u003e "},{"header":"2. Data, Variables And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Data source\u003c/h2\u003e\n\u003cp\u003eThe current study used the most recent Demographic and Health Survey of Bangladesh conducted during 2017-18 (hereby referred to as BDHS 2017-18). The National Institute for Population Research and Training (NIPORT) has conducted BDHS 2017-18 under the stewardship of the Ministry of Health and Family Welfare (MoHFW) of Bangladesh. The survey provided crucial information on information on childhood mortality levels, maternal and child health, fertility and fertility preferences, utilization of family planning methods, newborn care, women\u0026rsquo;s empowerment, selected non-communicable diseases (NCDS) and availability and accessibility of health and family planning services at the community level. The survey follows a two-stage stratified sample design. Further details regarding sample design, survey instruments, fieldwork and training of staff, data collection and processing, and response rates are available in the BDHS 2017-18 reports [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWe used the data for 8759 children aged under-five years born to 7562 mothers aged 15\u0026ndash;49 years in Bangladesh. However, we dropped the records of 361 children who were not alive during the survey period and had no information regarding their morbidity status. Therefore, the analytical sample for this study is 8398 children under five years of age.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Outcome variables\u003c/h2\u003e\n\u003cp\u003eThe outcome variable of morbidity status comes from the mother\u0026rsquo;s responses regarding the knowledge of their children\u0026rsquo;s morbidity. BDHS 2017-18 collected information regarding whether the children had suffered from fever, cough, and acute respiratory infections (ARI) within two weeks before the survey. We combined these three variables into a single variable of morbidity status with contained three categories which are \u0026ndash; children who did not suffer from any of the three morbidities (\u0026ldquo;no condition\u0026rdquo;); children who suffered from \u0026ldquo;single condition\u0026rdquo;; and children experiencing two and more conditions (\u0026ldquo;comorbidity\u0026rdquo;). The advantage of this approach is that it aloows us to take into account the severity of the children\u0026rsquo;s infirmity [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Explanatory variables\u003c/h2\u003e\n\u003cp\u003eGuided by extant research, we identified relevant factors that are associated with the occurrence of morbidity among children [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Accordingly, we included relevant explanatory variables, conditional upon their availability in BDHS 2017-18. The child-related characteristics are \u0026ndash; age in years (less than one, one, two, three, four) and gender (male, female). The parent-related characteristics are \u0026ndash; mother\u0026rsquo;s level of education (no formal education, upto primary, secondary and above), father\u0026rsquo;s level of education (no formal education, upto primary, secondary and above). The household-level factors are \u0026ndash; household sanitation condition (poor, average, good), household members drink treated water (no, yes), type of handwashing place (Private space, Public place, No handwashing place), shares toilet with other households (Not shared, Shared by two households (HH), Shared by three HH, Shared by four and more HH), wealth quintile (poorest, poor, middle, rich, richest), the religion of household head (Islam, Hinduism, others). Further, the season during the interview (Summer, Monsoon, Winter), place of residence (City Corporation, urban areas other than City Corporation, Rural areas), and administrative division (Dhaka, Chittagong, Barisal, Khulna, Mymensingh, Rajshahi, Rangpur, Sylhet) were also included.\u003c/p\u003e\n\u003cp\u003eTaking a cue from extant research, the household sanitation condition variable was constructed from three variables \u0026ndash; type of source of drinking water, type of sanitation facility and the number of members per room in the household [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Respondents were asked about the source of household drinking water and as per prevalent standards, we recoded the source of household drinking water into two categories \u0026ndash; \u0026ldquo;unimproved\u0026rdquo; coded as 0 (consisting of \u0026ldquo;dug, open well\u0026rdquo;, \u0026ldquo;river\u0026rdquo;, \u0026ldquo;pond\u0026rdquo;, \u0026ldquo;truck\u0026rdquo; and \u0026ldquo;bottled\u0026rdquo; categories from the original variable) and \u0026ldquo;improved\u0026rdquo; coded as 1 (consisting of \u0026ldquo;piped\u0026rdquo;, \u0026ldquo;tube well\u0026rdquo;, \u0026ldquo;hand pump\u0026rdquo;, \u0026ldquo;covered well\u0026rdquo; and \u0026ldquo;rainwater\u0026rdquo; categories from the original variable) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, we recoded the type of household toilet facility into \u0026ndash; \u0026ldquo;unimproved\u0026rdquo; coded as 0 (consisting of \u0026ldquo;defecation in open fields\u0026rdquo; and \u0026ldquo;traditional pit latrine\u0026rdquo; categories from the original variable) and \u0026ldquo;improved\u0026rdquo; coded as 1 (consisting of \u0026ldquo;ventilated improved pit latrine\u0026rdquo; and \u0026ldquo;flush toilet\u0026rdquo; categories from the original variable) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, households with less than 3 members per room were coded as \u0026ldquo;1\u0026rdquo; and those with 3 or more members were coded as \u0026ldquo;0\u0026rdquo;. After this, we added the three variables to obtain a household sanitation condition score. Households with a score of 3, score of 2 and score less than 2 were categorized as having \u0026ldquo;good\u0026rdquo;, \u0026ldquo;average\u0026rdquo; and \u0026ldquo;poor\u0026rdquo; sanitation condition respectively.\u003c/p\u003e\n\u003cp\u003eTo avoid multicollinearity, we coded a new wealth quintile variable after excluding information on household water source and toilet facility. The wealth quintile variable was prepared using standard procedures that are documented elsewhere [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Statistical methods\u003c/h2\u003e\n\u003cp\u003eWe performed bivariate and multivariate analysis to realize the study objectives. Owing to the categorical nature of the outcome variable, the bivariate association was examined using the chi-square test for association. Equivalently, multivariable analysis was performed by estimating multinomial regression models. The multivariate association of morbidity status of children with the explanatory variables was shown using relative risk ratios. Relative risk ratio gives the risk (multiple times) of having comorbidity (or single morbidity) compared to having no morbidity among those children belonging to a particular category of an explanatory variable given the effect of all the other explanatory variables remain constant [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. To show effectively the effect of different risk factors on child morbidity status, we have summarized the regression output into graphs of predicted probability [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWe checked for multicollinearity in the regression model and found the mean value of the variance inflation factor (VIF) to be less than 1.25. Therefore, multicollinearity is negligible [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Further, the Hausman-McFadden test revealed that our estimated model did not violate the independence from irrelevant alternatives (IIA) assumption [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. All statistical estimations were performed using the STATA software version 13.0 [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Sample description\u003c/h2\u003e\n\u003cp\u003eTable-1 shows the characteristics of 8,398 children aged under five years during BDHS 2017-18. Nearly 21% of children were in the age group less than 1 year and 52% of children were male. Nearly, 7% and 15% of children had a mother and father with no schooling education. One in every ten children comes from a household with poor sanitation conditions and 89% of children are from households where drinking water is untreated. Public space handwashing was common (64%) and most of the children did not share toilets with their family members (67%). Nearly, 27% of the population belongs to the poorest wealth quintile households and 65% reside in rural areas. In terms of population numeric, Chittagong is the largest division (17%) followed by the Dhaka division (15%) which includes the country\u0026rsquo;s capital city Dhaka.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAbsolute (N) and percentage (%) distribution of children under-five years by demographic, parent-related, household socio-economic and spatial covariates\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal population\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge of child (in years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTwo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOne\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,666\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLess than 1 year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender of child\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMother's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFather's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold sanitation condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWater treated before drinking\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of handwashing place\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo handwashing place\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eShares Toilet with other households\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot shared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 2 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 3 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 4 and more HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold wealth quintile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRichest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRich\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoorest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligion of household head\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIslam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHinduism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of season\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSummer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonsoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCity corporation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther urban areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCountry administrative division\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDhaka\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChittagong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBarisal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKhulna\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMymensingh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRajshahi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRangpur\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSylhet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable-2 provides the morbidity profile of under-five Bangladeshi children. We found that 54.5% of children had no morbidity within two weeks before the survey. In comparison, there were 18.4% of children in single morbid condition, co-morbidity was common among 27.1% of Bangladeshi children. Among all comorbid condition, children experiencing both fever and cough was high (15.1%). While only respiratory infection contributes to 0.5% of the population, however, the combined condition of respiratory infections accompanied by fever and cough increases the prevalence to 9% in the total population.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMorbidity profile of children under-five years\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMorbidity profile of children\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePopulation distribution\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eComorbidity status\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePopulation distribution\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo morbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo condition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOnly fever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSingle condition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOnly cough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e820\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOnly respiratory infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever and Cough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eComorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e27.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCough and Respiratory infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever and Respiratory infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever, Cough and Respiratory infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Bivariate analysis\u003c/h2\u003e\n\u003cp\u003eTable-3 shows the bivariate association between morbidity incidence and the explanatory variables. Morbidity condition was higher in children aged one year than those with other age categories (Single morbidity: 20.2%; Comorbidity: 33%). Approximately 29% of male children experienced comorbidity compared to 26% in females. Nearly 27.7% of children who drink untreated water experienced comorbidity compared to 23% of children who drink treated water. The incidence of comorbidity was higher if households do not practice handwashing (30.9%) in comparison to those households which used private (27.4%) and public space for handwashing (26.8%). Moreover, the incidence of comorbidity was higher during the monsoon season and in rural areas. Further, comorbidity incidence was higher (more than 31%) in the Barisal, Rajshahi, and Rangpur divisions. The association of morbidity status with stunting status, father\u0026rsquo;s education level, and household sanitation condition were not statistically significant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBivariate association between morbidity incidence and the demographic, parent-related, household socio-economic and spatial covariates\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eComorbidity status\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2 test of\u003c/p\u003e\n\u003cp\u003eassociation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epopulation\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSingle condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge of child (in years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;99.94;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTwo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e896\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOne\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,666\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e780\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLess than 1 year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender of child\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e739\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;11.29;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMother's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,439\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;14.97;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFather's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;3.44;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.488\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold sanitation condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;8.90;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWater treated before drinking\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;21.29;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of handwashing place\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,474\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e748\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;2.66;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.615\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,946\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,439\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo handwashing place\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eShares Toilet with other households\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot shared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;9.48;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.148\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 2 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 3 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 4 and more HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold wealth quintile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRichest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;23.18;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRich\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e770\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoorest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e666\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligion of household head\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIslam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;13.55;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHinduism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of season\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSummer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;7.07;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonsoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCity corporation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;39.06;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther urban areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,929\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCountry administrative division\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDhaka\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eꭓ2\u0026thinsp;=\u0026thinsp;57.30;\u003c/p\u003e\n\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChittagong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBarisal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKhulna\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMymensingh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRajshahi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRangpur\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSylhet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e696\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4,574\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e54.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1,545\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e18.4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2,279\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e27.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e\u003cstrong\u003eNote \u0026ndash; (a) Morbidity status of children categorized into No condition, Single condition, and Comorbidity\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Multivariate analysis\u003c/h2\u003e\n\u003cp\u003eTable-4 gives the multivariate association of morbidity status and the explanatory variables. Children aged one year were 1.40 [95% CI: 1.16, 1.67] and 2.01 [95% CI: 1.70, 2.36] times more likely to experience single morbidity and comorbidity respectively compared to children aged four years. We observe that male children were 1.18 [95% CI: 1.07, 1.31] times more likely to experience comorbidity compared to their female counterparts. Mothers with primary education show a lower chance of single morbid condition in their children [OR: 0.82; 95% CI: 0.71, 0.95]. Households with average sanitation conditions experience a higher probability of comorbidity than those with poor sanitation conditions [OR: 1.28; 95% CI: 1.07, 1.54]. Further, it is observed that children have a 1.64 [95% CI: 1.20, 2.26] times higher likelihood of comorbidity during the monsoon season in comparison to the summer season. Furthermore, children residing in rural areas and urban areas than city corporation involved 1.55 [95% CI: 1.22, 1.98] and 1.58 [95% CI: 1.25, 2.01] times greater risk of comorbidity respectively compared to living in areas under city corporation. In the multivariate analysis, the association of morbidity status with the father's education, religion and the facility of shared toilets loses its statistical significance. While observing the administrative division, Rajshahi [OR: 1.60; 95% CI: 1.24, 2.06] shows the higher likelihood of comorbidity followed by Barisal [OR: 1.53; 95% CI: 1.18, 2.00] and Rangpur [OR: 1.51; 95% CI: 1.17, 1.95] as compared to Dhaka.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRelative risk ratio showing the multivariate association between morbidity incidence and the demographic, parent-related, household socio-economic and spatial covariates\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMorbidity Status\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSingle condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRRR\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRRR\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge of child (in years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFour\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.76\u0026ndash;1.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.98\u0026ndash;1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTwo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.93\u0026ndash;1.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.43*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.21\u0026ndash;1.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOne\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.16\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.01*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.70\u0026ndash;2.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLess than 1 year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.95\u0026ndash;1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.39\u0026ndash;1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender of child\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.95\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.07\u0026ndash;1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMother's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.71\u0026ndash;0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.90\u0026ndash;1.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.60\u0026ndash;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.73\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFather's level of education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary and above\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpto primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.96\u0026ndash;1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.88\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.83\u0026ndash;1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.85\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold sanitation condition\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.95\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.07\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.96\u0026ndash;1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.01\u0026ndash;1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWater treated before drinking\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.97\u0026ndash;1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.86\u0026ndash;1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of handwashing place\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate space\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.81\u0026ndash;1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.74\u0026ndash;0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo handwashing place\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.75\u0026ndash;1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.76\u0026ndash;1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eShares Toilet with other households\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot shared\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 2 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.92\u0026ndash;1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.98\u0026ndash;1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 3 HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.92\u0026ndash;1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.97\u0026ndash;1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShared by 4 and more HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.77\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.80\u0026ndash;1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold wealth quintile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRichest\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRich\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.88\u0026ndash;1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.06\u0026ndash;1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.85\u0026ndash;1.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.96\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.92\u0026ndash;1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.88\u0026ndash;1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoorest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.84\u0026ndash;1.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.02\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligion of household head\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIslam\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHinduism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.73\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.73\u0026ndash;1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.22\u0026ndash;1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.12\u0026ndash;0.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType of season\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSummer\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.89\u0026ndash;1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.88\u0026ndash;1.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonsoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.95\u0026ndash;1.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.20\u0026ndash;2.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCity corporation\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther urban areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.80\u0026ndash;1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.58*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.25\u0026ndash;2.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.79\u0026ndash;1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.22\u0026ndash;1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCountry administrative division\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDhaka\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChittagong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.71\u0026ndash;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.00\u0026ndash;1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBarisal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.84\u0026ndash;1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.18\u0026ndash;2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKhulna\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.03\u0026ndash;1.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.91\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMymensingh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.92\u0026ndash;1.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.04\u0026ndash;1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRajshahi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.92\u0026ndash;1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.60*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.24\u0026ndash;2.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRangpur\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.79\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.51*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.17\u0026ndash;1.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSylhet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.70\u0026ndash;1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.08\u0026ndash;1.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of children\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e8,398\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote \u0026ndash; (a) RRR: relative risk ratio; (b) 95% Confidence Interval (CI) is given in brackets; (c) Statistical significance is denoted by asterisks where * denotes p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (d) \u0026reg; denotes reference category; (e) Morbidity status of children categorized into: no condition, single condition, comorbidity\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of the multivariate analysis were further confirmed by showing the predicted probability graphs for the factors having a statistically significant association with morbidity status. Figure-1 shows that the probability of acquiring single or comorbid (or multiple morbid) conditions increases with the decreasing age. Here, the highest single or comorbid condition is seen in the child\u0026rsquo;s first year of life. The probability of acquiring comorbidity condition among male children is higher, while little change in single morbidity condition is noticed across both genders (Figure-2). Figure-3 shows that the probability of comorbidity increases in the monsoon season among children, with little change in the summer and winter seasons. Children residing in other urban areas show a higher probability of comorbid conditions (figure-4). While observing the administrative division of Bangladesh, a scattered picture is noticed. The probability of comorbidity condition was higher at the Rajshahi division (figure-5).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough children are not the face of the COVID-19 pandemic, the impact of this universal crisis can be lifelong for them [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. COVID-19 mitigation strategies have usually enforced social distancing and isolation measures. However, such strategies may sometimes disrupt life-saving health services. Studies have shown that a sudden outbreak of pandemic had affected regular health care facilities and services [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In Bangladesh, acute respiratory infections (ARIs) are the most common morbidity condition which needs proper attention throughout the year. So, keeping in view the COVID-19 situation, the present study uses recent national-level data of Bangladesh to highlight the vulnerability factors of under-five childhood morbidity.\u003c/p\u003e\n\u003cp\u003eWe found a strong effect of age on the incidence of ARIs along with fever and cough and these results are consistent with the previous Bangladesh studies [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. It has been usually found that children at their younger ages can get exposed to contaminated water, soil, and food easily as at these ages they usually crawl and tries to explore the environment. However, older ages children who have already moved towards this exposure are well-versed with their environment and sometimes build a strong immunity till that age. Incidence of comorbidity condition among under-five children also varies according to household wealth index in both single and multiple morbidity conditions. Multiple morbidities were found to be significantly higher among monsoon seasons which is consistent with a previous study showing the health impact of climate change [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, in contrast to a previous Bangladesh study, the present study shows that comorbidity condition is higher among male children than females [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Rajshahi administrative division followed by Barisal and Rangpur shows the highest probability of comorbid condition. This may be due to the higher indigenous population in this area. Also, a WHO report has shown that throughout the decade, poverty in few administrative divisions like Rangpur had increased facing a weak health care system [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Results from predicted probability also confirm the pre-existing demographic risk factors of under-five childhood morbidity in Bangladesh. As the age and sex of the child, place of residence and administrative division of Bangladesh emerged as the detrimental factor for under-five morbidity. Water and sanitation condition (like sharing toilets with more people in a household) doesn\u0026rsquo;t affect significantly the morbidity status. These findings are consistent with a Bangladesh study where improved water and sanitation sources were not found to be significantly associated with childhood morbidity [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Further, another study had also provided evidence that water, sanitation and handwashing interventions did not affect the linear growth of children in Bangladesh [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. This might be due to the reason that most of the Bangladeshi population lack proper access to improved water and sanitation sources. And the combined effect of both water and sanitation interventions should be considered for bringing favourable changes [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Also, there is the necessity to consider the other unobserved factors which may play role in comorbid conditions.\u003c/p\u003e\n\u003cp\u003eThe current pandemic can even worsen the situation of food insecurity, poverty, hunger, and malnutrition across the world. Previous studies have also shown the impact of the pandemic on the mental health of children [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although the government had taken different measures to protect the well-being of children, the pandemic had increased the existing inequities and burdened the country with the risk of childhood disease or death. So, the unprecedented situation of COVID-19 draws our attention towards strengthening the public care facilities and identify the vulnerability factors which lead to the morbidity condition of children. The present study is also backed with the recent national-level data of under-five children in Bangladesh which will help us to evaluate the situation just before the pandemic. Our study will also help policymakers to explore different mitigation strategies.\u003c/p\u003e\n\u003cp\u003eHowever, our study has some limitations too. First, our study provides only a cross-sectional view of the scenario and therefore does not allow us to examine causality. Second, the morbidity incidence was evaluated from the self-reported information provided by women. However, the short recall period of morbidity (two weeks before the survey) makes the chances of recall bias minimal. Also, there is a need to consider the unobserved factors which affect the association.\u003c/p\u003e"},{"header":"5. Conclusion","content":" \u003cp\u003eThe nationwide lockdown due to the COVID-19 pandemic had not only isolated the people from physical communication but also disrupted the health care facilities critical for mitigating the pre-existing morbidity condition among Bangladeshi children. During the pandemic, it was found that the access to regular health care services and continuity of care become worsened. Our study urges a greater investment by the government to mitigate the adverse impact of the pandemic and to enhance the programs which can reduce the effect of vulnerability factors. Although some of our findings suggest that individual intervention of water and sanitation may have no big advantages, both individual and combined investments are required according to delivering convenience. This may be promoted by sensitisation of individuals about insightful strategies to prevent infectious diseases in children right from their homes by focusing on their biological vulnerabilities.\u003c/p\u003e "},{"header":"6. List Of Abbreviations","content":"\u003cp\u003eARIs: Acute Respiratory Tract Infections\u003c/p\u003e\n\u003cp\u003eNCDs: Non-communicable Diseases\u003c/p\u003e\n\u003cp\u003eBDHS: Bangladesh Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus Disease-2019\u003c/p\u003e\n\u003cp\u003eUNICEF: United Nations International Children\u0026rsquo;s Emergency Fund\u003c/p\u003e\n\u003cp\u003eNIPORT: National Institute for Population Research and Training\u003c/p\u003e\n\u003cp\u003eMoHFW: Ministry of Health and Family Welfare\u003c/p\u003e"},{"header":"7. Disclosure Statements","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is freely available in the public domain and survey agencies that conducted the field survey for the data collection have collected prior consent from the respondent. The local ethics committee of the International Institute for Population Sciences (IIPS), Mumbai, ruled that no formal ethics approval was required to research this data source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study uses a secondary source of data that is freely available in the public domain through:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2017.cfm?flag=0\"\u003ehttps://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2017.cfm?flag=0\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors did not receive any funding to carry out this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concept was drafted by RR; RP contributed to the analysis design, RP and RR advised on the paper and assisted in paper conceptualization. RP and RR contributed to the comprehensive writing of the article. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to Dr Hemkothang Lhungdim and Dr Harihar Sahoo of the International Institute for Population Sciences (IIPS) for their insightful comments and suggestion on an earlier version of this paper which was presented at the IIPS Seminar 2021.\u003c/p\u003e"},{"header":"8. References","content":"\u003col\u003e\n\u003cli\u003eRate C-F. Characteristics of Patients Dying in Relation to COVID-19 in Italy Onder G, Rezza G, Brusaferro S. JAMA Published online March. 2020;23.\u003c/li\u003e\n\u003cli\u003eUN. UN Policy Brief: The Impact of COVID‐19 on children. 2020.\u003c/li\u003e\n\u003cli\u003eRoberton T, Carter ED, Chou VB, Stegmuller AR, Jackson BD, Tam Y, et al. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study. The Lancet Global Health. 2020;8:e901\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBalabanova D, Mills A, Conteh L, Akkazieva B, Banteyerga H, Dash U, et al. Good Health at Low Cost 25 years on: lessons for the future of health systems strengthening. The Lancet. 2013;381:2118\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eKamal MM, Hasan MM, Davey R. Determinants of childhood morbidity in Bangladesh: evidence from the demographic and health survey 2011. BMJ open. 2015;5:e007538.\u003c/li\u003e\n\u003cli\u003eLiu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000\u0026ndash;15: an updated systematic analysis with implications for the Sustainable Development Goals. The Lancet. 2016;388:3027\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eJamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al. Disease control priorities in developing countries. The World Bank; 2006.\u003c/li\u003e\n\u003cli\u003eSelvaraj K, Chinnakali P, Majumdar A, Krishnan IS. Acute respiratory infections among under-5 children in India: A situational analysis. Journal of natural science, biology, and medicine. 2014;5:15.\u003c/li\u003e\n\u003cli\u003eMonto AS, Ullman BM. Acute respiratory illness in an American community: the Tecumseh study. Jama. 1974;227:164\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eValderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. The Annals of Family Medicine. 2009;7:357\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eHazelden F. The Stress-Vulnerability Model | Behavioral Health Evolution. 2016. https://www.bhevolution.org/public/stress-vulnerability.page.\u003c/li\u003e\n\u003cli\u003eBlack RE. Diarrheal diseases and child morbidity and mortality. Population and Development Review. 1984;10:141\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eRichardson A. Factors influencing acute respiratory infection of children in Bangladesh. International Journal of Statistics and Systems. 2013;8:239\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eFerdous F, Das SK, Ahmed S, Farzana FD, Malek MA, Das J, et al. Diarrhoea in slum children: observation from a large diarrhoeal disease hospital in D haka, B angladesh. Tropical Medicine \u0026amp; International Health. 2014;19:1170\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eAzad SMY, Bahauddin KM, Uddin MH, Parveen S. Indoor air pollution and prevalence of acute respiratory infection among children in rural area of Bangladesh. Indoor Air. 2014;4.\u003c/li\u003e\n\u003cli\u003eAzad KMAK. Risk factors for acute respiratory infections (ARI) among under-five children in Bangladesh. Journal of Scientific Research. 2009;1:72\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003ePinz\u0026oacute;n-Rond\u0026oacute;n \u0026Aacute;M, Aguilera-Otalvaro P, Z\u0026aacute;rate-Ardila C, Hoyos-Mart\u0026iacute;nez A. Acute respiratory infection in children from developing nations: a multi-level study. Paediatrics and international child health. 2016;:1\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLuby SP, Rahman M, Arnold BF, Unicomb L, Ashraf S, Winch PJ, et al. Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Bangladesh: a cluster randomised controlled trial. The Lancet Global Health. 2018;6:e302\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eUNICEF. 33 million children in Bangladesh live in poverty. 2009. 2009. https://www.unicef.org/media/media_51925.html.\u003c/li\u003e\n\u003cli\u003eRahman M, Ashraf S, Unicomb L, Mainuddin AKM, Parvez SM, Begum F, et al. WASH Benefits Bangladesh trial: system for monitoring coverage and quality in an efficacy trial. Trials. 2018;19:1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eTofail F, Fernald LCH, Das KK, Rahman M, Ahmed T, Jannat KK, et al. Effect of water quality, sanitation, hand washing, and nutritional interventions on child development in rural Bangladesh (WASH Benefits Bangladesh): a cluster-randomised controlled trial. The Lancet Child \u0026amp; Adolescent Health. 2018;2:255\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eHedrick S. Water In Crisis - Spotlight Banglasdesh. The Water Project. 2016. https://thewaterproject.org/water-crisis/water-in-crisis-bangladesh.\u003c/li\u003e\n\u003cli\u003eHalder AK, Gurley ES, Naheed A, Saha SK, Brooks WA, El Arifeen S, et al. Causes of early childhood deaths in urban Dhaka, Bangladesh. PLoS One. 2009;4:e8145.\u003c/li\u003e\n\u003cli\u003eNIPORT, Ministry of Health and Family Welfare \u0026amp; I. Bangladesh Demographic and Health Survey, 2017-18. NIPORT; 2020.\u003c/li\u003e\n\u003cli\u003ePaul R, Singh A. Does early childhood adversities affect physical, cognitive and language development in indian children? Evidence from a panel study. SSM-Population Health. 2020;12:100693.\u003c/li\u003e\n\u003cli\u003eWHO-UNICEF. MEETING THE MDG DRINKING WATER SANITATION TARGET A N D A Mid-Term Assessment of Progress. 2004.\u003c/li\u003e\n\u003cli\u003eRutstein SO, Johnson K. The DHS wealth index. DHS comparative reports no. 6. Calverton: ORC Macro. 2004.\u003c/li\u003e\n\u003cli\u003eCameron AC, Trivedi PK. Microeconometrics: Methods and Applications . Cambridge University Press. 2005. http://cameron.econ.ucdavis.edu/mmabook/mma.html. Accessed 23 Jun 2020.\u003c/li\u003e\n\u003cli\u003eLong JS, Freese J. Regression models for categorical dependent variables using Stata. Stata press; 2006.\u003c/li\u003e\n\u003cli\u003eEnder P. collin\u0026rdquo;: Stata command to compute collinearity diagnostics. 2010.\u003c/li\u003e\n\u003cli\u003eCheng S, Long JS. Testing for IIA in the multinomial logit model. Sociological methods \u0026amp; research. 2007;35:583\u0026ndash;600.\u003c/li\u003e\n\u003cli\u003eStataCorp LP. Stata 13. College Station: StataCorp LP. 2013.\u003c/li\u003e\n\u003cli\u003eCash R, Patel V. Has COVID-19 subverted global health? The Lancet. 2020;395:1687\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eKhan AE, Xun WW, Ahsan H, Vineis P. Climate change, sea-level rise, \u0026amp; health impacts in Bangladesh. Environment: Science and Policy for Sustainable Development. 2011;53:18\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eChen LC, Huq E, d\u0026rsquo;Souza S. Sex bias in the family allocation of food and health care in rural Bangladesh. Population and development review. 1981;:55\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eWorld Bank Group. Bangladesh Poverty Assessment Facing old and new frontiers in poverty reduction. 2019.\u003c/li\u003e\n\u003cli\u003eBegum S, Ahmed M, Sen B. Do water and sanitation interventions reduce childhood diarrhoea? New evidence from Bangladesh. The Bangladesh Development Studies. 2011;:1\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eYeasmin S, Banik R, Hossain S, Hossain MN, Mahumud R, Salma N, et al. Impact of COVID-19 pandemic on the mental health of children in Bangladesh: A cross-sectional study. Children and youth services review. 2020;117:105277.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Comorbidity, Children under-five years, Infectious diseases, Respiratory Infection, Biological vulnerability, Vulnerability to COVID-19, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-477731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-477731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Acute respiratory tract infections (ARIs) are the leading infectious disease worldwide and continues to be the single largest morbidity contributor in children. One of the most densely populated countries, Bangladesh also threatens by alarming under-five childhood morbidity, which has aggravated in past years with the COVID-19 pandemic. This study attempts to understand the biological factors affecting the pre-existing respiratory tract infections in under 5 children of Bangladesh.\u003c/p\u003e\u003cp\u003eMethods: The present study uses data from 8398 children aged below 5 years during the survey from the Demographic and Health Survey of Bangladesh (BDHS 2017-18). Both bivariate and multivariate analyses were performed to understand the biological vulnerability factors of pre-existing acute respiratory tract infections (ARIs) in under five Bangladeshi children and relate them with the potential impact of the COVID-19 pandemic. Further, to show effectively the effect of different risk factors on child morbidity status, we have summarized all the results into prediction graphs at various levels of one variable as the other variable changes\u003c/p\u003e\u003cp\u003eResults: Children aged one year were 1.40 [95% CI: 1.16, 1.67] and 2.01 [95% CI: 1.70, 2.36] times more likely to experience single morbidity and comorbidity respectively compared to children aged four years. We observe that male children were 1.18 [95% CI: 1.07, 1.31] times more likely to experience comorbidity compared to their female counterparts. Prediction graphs confirm the multivariate analysis as the probability of comorbidity remains higher in the monsoon season among children, with little change in the summer and winter seasons. Further, Rajshahi administrative division followed by Barisal and Rangpur shows the highest probability of comorbid condition in Bangladesh.\u003c/p\u003e\u003cp\u003eConclusion: Biological factors emerged as the prominent contributor in child ARIs condition. More care is required as the nationwide lockdown due to the COVID-19 pandemic had not only isolated the people from physical communication but also disrupted the health care facilities to care for the pre-existing morbidity condition among Bangladeshi children. Insightful strategies are required to prevent infectious diseases in children right from their homes by focusing on their biological vulnerabilities.\u003c/p\u003e","manuscriptTitle":"Assessing Biological Vulnerability of Acute Respiratory Tract Infection Among Children: Evidence from Bangladesh Demographic and Health Survey 2017-18","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-07 13:45:34","doi":"10.21203/rs.3.rs-477731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffaa1e90-66d9-4737-8b41-17bc22311cb5","owner":[],"postedDate":"May 7th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4154763,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-05-10T07:44:03+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-07 13:45:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-477731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-477731","identity":"rs-477731","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}