OR IG IN AL AR TI CL E International Health 2024; 16 : 97–106 https://doi.org/10.1093/inthealth/ihad046 Advance Access publication 30 June 2023 Prevalence and determinants of diarrhoea and acute respiratory infections among children aged under five years in West Africa: evidence from demographic and health surveys Derrick Nyantakyi Owusu a , b , ∗, Henry Ofori Duahc , Duah Dwomohd and Yakubu Alhassand a Research Department, FOCOS Orthopaedic Hospital, P.O.Box KD 779, Accra-Ghana; b Department of Population, Family and Reproductive Health, School of Public Health, College of Health Sciences, University of Ghana, Legon, P.O. Box LG 118, Accra, Ghana; c College of Nursing, University of Cincinnati, Cincinnati 45221, Ohio; d Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Legon, P.O. Box LG 118, Accra, Ghana ∗Corresponding author: Tel: + 233547505328; E-mail: owusuwesley@gmail.com Received 8 November 2022; revised 14 April 2023; editorial decision 2 June 2023; accepted 8 June 2023 Background: Diarrhoea and pneumonia are the leading causes of morbidity and mortality in children aged < 5 y (under five) globally. This study sought to investigate the prevalence and determinants of diarrhoea and acute respiratory infections (ARIs) among children under five in West Africa. Methods: The most recent demographic and health survey (DHS) standard for 13 West African countries was used in the study. We calculated the prevalence of diarrhoea and ARIs (2 wk prior to the survey) and performed multivariable complex logistic regression analysis to identify possible predictors of diarrhoea and ARIs. Results: The weighted prevalence of diarrhoea and ARI was 13.7% and 15.9%, respectively. The prevalence of comorbid diarrhoea and ARI was 4.4%. Children aged < 2 y (p < 0.001), mothers aged < 30 y (p < 0.003), mothers without formal education (p < 0.001), poor households (p < 0.001) and poor nutritional status, wasting (p = 0.005) and underweight (p < 0.001), were the independent predictors of diarrhoea. The independent predictors of ARIs were children with no childhood vaccinations (p = 0.002), use of solid fuel in the household (p = 0.007), being underweight (p = 0.05) and diarrhoea (p < 0.001). Conclusions: The findings imply the need for holistic public health interventions such as increased vaccination coverage, population-based nutritional programmes and campaigns on the use of cleaner cooking fuel targeted at high-risk subgroups in the population to reduce the burden and adverse effects of diarrhoea and ARIs in the West African region. Keywords: ARI, children under five, demographic health surveys, diarrhoea. I D i d m i l fi T p p high prevalence of diarrhoea (15.3%) and pneumonia (25.3%) in sub-Saharan Africa (SSA) coupled with the burden of other co- morbid diseases and poverty.5 , 6 Numerous interventions have been implemented globally in recent years to combat the prevalence and impact of diarrhoea and ARIs in children. These initiatives have been focused on various fronts, including improved access to clean water and sanitation facilities, vaccination campaigns and community ed- ucation programmes. These interventions have all played an im- portant role in reducing the burden of diarrhoea and ARIs in chil- dren; however, prevalence is still high in the SSA regions. © a p D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 ntroduction iarrhoea and acute respiratory infections (ARIs) remain lead- ng causes of morbidity and mortality in children aged < 5 y (un- er five) worldwide.1 According to the most recent WHO esti- ates, there were approximately 149 million cases of diarrhoea n children under five in 2019, resulting in an estimated 1.4 mil- ion deaths. Also, about 42 million cases of ARIs in children under ve were reported, resulting in an estimated 630 000 deaths.1 he burden of under-five mortality attributable to diarrhoea and neumonia is higher for children in developing countries com- ared with developed regions.2 –4 This may be attributable to the The Author(s) 2023. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. This is an Open Access rticle distributed under the terms of the Creative Commons Attribution License ( https:// creativecommons.org/ licenses/ by/ 4.0/ ), which ermits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 97 https://doi.org/10.1093/inthealth/ihad046 https://orcid.org/0000-0001-7831-1392 mailto:owusuwesley@gmail.com https://creativecommons.org/licenses/by/4.0/ D. N. Owusu et al. Table 1. Distribution of unweighted frequencies for West African countries Country Unweighted frequency Unweighted % Burkina Faso 13 716 10 .34 Benin 12 651 9 .54 Cote d’Ivoire 7093 5 .35 Ghana 5595 4 .22 Gambia 7927 5 .98 Guinea 7273 5 .49 Liberia 5245 3 .96 Mali 9275 7 .00 Nigeria 30 713 23 .16 Niger 11 602 8 .75 Serra Leone 9063 6 .84 Senegal 5899 4 .45 Togo 6535 4 .93 Total 132 587 D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 With just 2 y to the timeline of the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea, which aims to reduce death from pneumonia and diarrhoea by 2025, it ap- pears that more focused interventions and research in SSA are required. Some studies have reported that the high prevalence of diarrhoea and ARIs is associated with a myriad of sociodemo- graphic factors, such as the child’s nutritional status and age, sanitation, caregiver’s education, mother’s occupation, wealth quintile, source of drinking water, hand hygiene and Rota virus vaccination, among others.7 –9 West Africa has a high burden of diarrhoea and ARIs in children under five because of various factors such as poor sanitation, lim- ited access to clean water, inadequate hygiene practices, malnu- trition and low vaccination coverage. These factors contribute to the transmission and persistence of infectious agents that cause diarrhoea and ARIs.10 Additionally, poverty, weak health systems and limited access to healthcare services make it difficult for af- fected children to receive timely and appropriate treatment, lead- ing to increased morbidity and mortality.10 Therefore, addressing these underlying factors and strengthening healthcare systems is crucial for reducing the burden of diarrhoea and ARIs in chil- dren under five in West Africa. Although there is a wealth of in- formation on the prevalence and determinants of diarrhoea and ARIs in specific sites or countries, there is a paucity of literature estimating the prevalence of diarrhoea and ARIs among children under five in West Africa. Hence, this study aims to investigate the prevalence and determinants of diarrhoea and ARIs among children under five in the West African subregion. Methods Overview of the demographic and health survey The demographic and health survey (DHS) is a nationally rep- resentative cross-sectional survey. The DHS receives technical assistance from International Classification of Functioning, Dis- ability and Health and is predominantly funded by the United States Agency for International Development. Moreover, the sur- vey also receives supplementary support from various interna- tional donors, including UNICEF, UNFPA, The World Bank and the Global Fund, among others. Study population Data from DHSs of 13 West African countries were used in this study. The surveys, which are conducted at average intervals of 5 y in every country, are cross-sectional and provide information on health and population characteristics. The most recent DHS for various West African countries was used. The countries involved were Burkina Faso (2010), Benin (2017), Cote d’Ivoire (2011– 2012), Ghana (2014), Gambia (2019), Guinea (2018), Liberia (2019), Mali (2018), Nigeria (2018), Niger (2017), Serra Leone (2019), Senegal (2010–2011) and Togo (2013). The study was conducted in 13 countries in the West Africa subregion, namely, Benin, Burkina Faso, Cote d’Ivoire, Gambia, Ghana, Guinea, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo. These are all developing countries, where child mor- tality and morbidity rates are among the highest in the world. In total, 132 587 mothers from various West African countries 98 were interviewed for the study. Nigeria had the most participants (23.16%) across the various samples. Burkina Faso had the sec- ond highest number, accounting for 10.34% of the overall sample size. Liberia was the country that contributed the least, account- ing for 3.96% of the total (Table 1 ). Study variables Diarrhoea and ARI were the dependent (outcome) variables. Codes 1 and 0 denoted the presence and absence of outcome variables, respectively. ARI prevalence was calculated by asking mothers with children under five whether their child had been ill with a cough, short, quick breaths and difficulty breathing as a result of a chest condition in the 2 wk preceding the DHS. Diarrhoea prevalence was determined by asking mothers of children born 5 y before the survey if their children experienced di- arrhoea episodes in the 2 wk preceding the survey. The child’s age (in months), gender, location of residence (rural or urban), wealth quintile, caregiver’s age, caregiver’s education, water source, toi- let facility (improved or unimproved), nutritional status (wasting, stunting and underweight), vaccination status (ever or never vac- cinated), breastfeeding status (ever or never breastfed) and have had vitamin A in the last 6 mo (yes or no), were all independent exposure factors. The household wealth index was determined by considering various household characteristics (such as the source of drink- ing water, type of toilet, shared facilities, roofing material, wall and floor materials and cooking fuel), as well as household as- sets (such as ownership of a television, radio, vehicles, bicycles, motorcycles, watches, agricultural land and farm animals). Us- ing factor analysis, weights were assigned to each asset within each household, resulting in a cumulative score. Subsequently, households were ranked based on these cumulative scores. The distribution of the overall wealth score was evaluated, and four specific thresholds were identified to establish quintiles. These distinct thresholds were utilised to categorise household wealth International Health i r m p a a a h h a n w ‘ u D P i N a m t t p d q t v T a T R S T t 1 a ( ( ( d i i O t h e h w t i W a u t P fi T p l G M S t d P i T p B ( ( S b i d w R T a m t A e v s M o c T a c t o o c j d m d D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 nto quintiles, which were denoted as the poorest, poorer, middle, icher and richest categories, respectively, based on their place- ent within the following percentiles: equal to or below the 20th ercentile, above 20% but not exceeding the 40th percentile, bove the 40th percentile but not exceeding the 60th percentile, bove the 60th percentile but not exceeding the 80th percentile, nd above the 80th percentile. Also, based on WHO criteria, stunting was operationalised as eight-for-age Z scores < –2; wasting was defined as weight-for- eight Z scores < –2; and underweight was defined as weight-for- ge Z scores < –2. For the purpose of this study, each of the three utritional indicators was coded separately as binary variables, ith ‘1’ coded for stunting, wasting and underweight, whereas 0’ was used as the code for the absence of stunting, wasting and nderweight. ata analysis rior to data analysis, we performed data cleaning and recoding n Stata version 16 (Stata Corporation, College Station, TX, USA). ext, the data were weighted, allowing us to perform univariate nalysis by accounting for survey design. Prior to bivariate and ultivariable analysis, complex survey mode was activated using he ‘svyset’ Stata command to enable the adjustment for clus- ers, stratification and sample weights. This helps to account for ossible analytical errors that are embedded within secondary atasets collected using complex sampling designs.11 Subse- uently, bivariate analyses with the χ2 test were conducted o assess the relationship between the selected independent ariables and the two dependent variables (diarrhoea and ARI). he strength of the association was estimated using crude and djusted logistic regression analyses with the ‘logistic’ command. hese were performed separately for diarrhoea and ARI. esults ample characteristics he total number of children aged 0–59 mo involved in he study was 75 146. The majority of children were aged 2–35 mo (40.2%). The same age category was high across ll countries: Burkina Faso (39.8%), Benin (39.1%), Cote d’Ivoire 42.6%), Ghana (41.2%), Gambia (39.9%), Guinea (38.3%), Liberia 37.6%), Mali (40.2%), Nigeria (40.1%), Niger (38.5%), Serra Leone 39.4%), Senegal (42.0%) and Togo (42.1%). The majority of chil- ren sampled in the study were male (50.6%). The results of nutritional assessments carried out on children n the study were as follows: 31% of children experienced stunt- ng, 20.0% were underweight and 8.4% experienced wasting. verall, 49.8% of mothers were in the 25–34 y age category. More han one-half (54%) of mothers who participated in the study ad no formal education. Regarding individual countries, Nigeria merged as the country with the highest number of mothers who ave had tertiary education, accounting for 8.6%. After Nigeria as Ghana, with 4.5% of mothers who have had tertiary educa- ion. Furthermore, most mothers (43.3%) were in the poor wealth ndex. For the type of cooking fuel used at home by mothers in est Africa, solid fuel emerged as the most used cooking fuel, ccounting for 87.0%. The majority (53.7%) of participants were sing unimproved toilet facilities; 70% of participants had access o improved water sources (Table 2 ). revalence of diarrhoea among children under ve in West Africa he overall prevalence of diarrhoea in West Africa was 13.7%. The revalence of diarrhoea across West African countries was as fol- ows: Burkina Faso (14.9%), Benin (10.5%), Cote d’Ivoire (18.5%), hana (11.9%), Gambia (19.7%), Guinea (14.6%), Liberia (16.3%), ali (17.2%), Nigeria (12.8%), Niger (14.4%), Serra Leone (7.2%), enegal (13.7%) and Togo (15.2%) (Figure 1 ). From the distribu- ion, Gambia was the country with the most children who had iarrhoea 2 wk before the survey, followed by Cote d’Ivoire. revalence of ARI among children under five n West Africa he overall prevalence of ARI for West Africa was 15.9%. The revalence of ARI across West African countries were as follows: urkina Faso (10.3%), Benin (18.5%), Cote d’Ivoire (22.1%), Ghana 14.0%), Gambia (20.9%), Guinea (13.0%), Liberia (25.3%), Mali 12.6%), Nigeria (15.7%), Niger (14.5%), Serra Leone (14.2%), enegal (19.8%) and Togo (27.9%) (Figure 2 ). From the distri- ution, Togo was the country with the highest burden of ARIs n children 2 wk before the survey, followed by Liberia and Cote ’Ivoire. Furthermore, prevalence for comorbid ARI and diarrhoea as 4.4%. esults of bivariate analysis he following variables had a significant association with di- rrhoea prevalence in bivariate analysis: residence, child’s age, other’s age, mother’s educational level, wealth index, sanita- ion, water source and nutritional status (Table 3 ). Also, the following variables had a significant association with RI prevalence: residence, child’s age, mother’s age, mother’s ducational level, sanitation, breastfeeding, vaccination status, itamin A in the last 6 mo, type of cooking fuel and nutritional tatus (Table 3 ). ultivariable logistic regression estimates f predictors of diarrhoea and ARI among hildren under five in West Africa he strength of the association was measured using a multivari- ble logistic regression model. The model was adjusted for age of hild, gender of child, age of mother, place of residence, educa- ion level of mother, household wealth index, sanitation, source f drinking water, vaccination status, vitamin A supplement, type f cooking fuel (solid, liquid or clear fuel) and nutritional status. Compared with children aged 24–59 mo, there was a 57% in- reased probability of diarrhoea in children aged 6–23 mo (ad- usted OR [aOR] = 1.57, 95% CI 1.30 to 1.90). Compared with chil- ren whose mothers were aged < 30 y, those children whose others were aged > 30 y were 17% less likely to have had iarrhoea 2 wk before the survey (aOR = 0.83, 95% CI 0.74 to 99 D. N. Owusu et al. Table 2. Distribution of characteristics across West African countries Countries Burkina Faso (%) Benin (%) Cote d’Ivoire (%) Ghana (%) Gambia (%) Guinea (%) Liberia (%) Mali (%) Nigeria (%) Niger (%) Sierra Leone (%) Senegal (%) Togo (%) Total (%) Characteristics Child age (mo) < 6 11 .4 11 .5 11 .8 12 .1 13 .2 12 .4 12 .0 11 .8 10 .7 13 .2 12 .5 11 .5 9 .2 11 .4 6–11 10 .7 12 .4 12 .3 10 .6 10 .4 9 .0 13 .4 10 .2 10 .9 10 .2 11 .7 11 .4 11 .4 10 .9 12–35 39 .8 39 .1 42 .6 41 .2 39 .9 38 .3 37 .6 40 .2 40 .1 38 .5 39 .4 42 .0 42 .1 40 .2 36–47 19 .4 19 .0 17 .8 18 .7 19 .9 21 .1 19 .3 19 .5 19 .4 20 .5 18 .7 18 .5 19 .5 19 .3 48–59 18 .4 18 .1 15 .5 17 .4 16 .6 19 .2 17 .7 18 .3 18 .9 17 .6 17 .7 16 .6 17 .7 18 .1 Gender Male 50 .5 50 .6 48 .9 52 .0 51 .8 51 .7 50 .0 50 .7 50 .8 50 .4 50 .4 48 .9 50 .1 50 .6 Female 49 .5 49 .4 51 .1 48 .0 48 .2 48 .3 50 .0 49 .3 49 .2 49 .6 49 .6 51 .1 49 .9 49 .4 Mother’s age (y) 15–24 28 .60 25 .30 31 .10 20 .90 21 .40 28 .10 34 .30 29 .20 23 .60 27 .30 28 .70 22 .90 21 .10 25 .20 25–29 26 .00 30 .90 28 .10 25 .20 31 .20 27 .50 25 .60 27 .70 28 .30 27 .80 27 .60 25 .60 28 .40 27 .80 30–39 36 .20 35 .60 32 .80 43 .00 39 .00 34 .60 31 .00 35 .40 38 .90 36 .10 35 .40 41 .20 40 .00 37 .80 ≥40 9 .20 8 .20 8 .00 11 .00 8 .40 9 .80 9 .10 7 .7 9 .20 8 .90 8 .30 10 .30 10 .50 9 .20 Mother’s education No education 92 .3 82 .3 81 .3 41 .4 59 .0 84 .0 57 .2 81 .5 49 .6 94 .3 64 .9 80 .5 68 .9 63 .4 Primary/JSH 7 .0 15 .9 16 .5 47 .2 29 .1 11 .8 29 .9 17 .2 21 .1 5 .2 28 .2 16 .0 28 .8 19 .9 SHS 0 .2 0 .5 1 .1 7 .0 7 .8 2 .0 9 .1 0 .0 20 .6 0 .1 4 .1 1 .0 0 .8 11 .5 Tertiary 0 .6 1 .4 1 .0 4 .5 4 .1 2 .2 3 .9 1 .3 8 .6 0 .4 2 .8 2 .5 1 .6 5 .3 Residence Urban 17 .3 38 .9 37 .7 45 .1 65 .7 29 .7 53 .7 20 .7 39 .6 13 .7 35 .3 36 .7 36 .3 34 .9 Rural 82 .7 61 .1 62 .3 54 .9 34 .3 70 .3 46 .3 79 .3 60 .4 86 .3 64 .7 63 .3 63 .7 65 .1 Wealth index Poor 41 .80 41 .50 47 .00 43 .0 43 .50 44 .90 45 .80 41 .70 43 .50 40 .0 45 .60 46 .10 41 .10 43 .30 Middle 21 .7 20 .20 19 .40 19 .60 20 .80 19 .70 18 .70 21 .60 20 .60 20 .50 20 .40 18 .20 20 .10 20 .40 Rich 36 .5 38 .30 33 .60 37 .40 35 .60 35 .40 35 .40 36 .80 35 .60 39 .50 34 .00 35 .70 38 .80 36 .30 Sanitation Improved 24 .9 27 .7 41 .2 64 .8 62 .6 48 .0 43 .4 53 .6 50 .6 17 .7 50 .0 67 .3 34 .1 46 .3 Unimproved 75 .1 72 .3 58 .8 35 .2 37 .4 52 .0 56 .6 46 .4 49 .4 82 .3 50 .0 32 .7 65 .9 53 .7 Water source Improved 74 .9 65 .7 73 .9 84 .0 89 .4 76 .1 80 .0 67 .0 67 .6 65 .9 60 .4 81 .6 60 .6 70 .0 Unimproved 25 .1 34 .3 26 .1 16 .0 10 .6 23 .9 20 .0 33 .0 32 .4 34 .1 39 .6 18 .4 39 .4 30 .0 Breastfeeding Ever breastfed 98 .2 96 .1 95 .7 98 .9 99 .0 87 .1 98 .1 94 .1 98 .4 98 .1 98 .1 98 .2 98 .3 97 .6 Never breastfed 1 .8 3 .9 4 .3 1 .1 1 .0 12 .9 1 .9 5 .1 1 .6 1 .9 1 .9 1 .8 1 .7 2 .4 Vaccination status Ever vaccinated 86 .7 67 .4 84 .8 87 .7 81 .7 58 .9 82 .2 64 .3 67 .4 84 .0 87 .7 82 .3 88 .2 73 .4 Never vaccinated 13 .3 32 .6 15 .2 12 .2 12 .3 18 .3 17 .8 35 .7 32 .6 16 .0 12 .3 17 .7 11 .8 26 .6 Vitamin A in last 6 mo Yes 60 .4 48 .5 57 .6 58 .7 49 .1 37 .2 41 .6 61 .9 41 .4 55 .5 62 .4 43 .4 78 .3 48 .4 No 39 .6 51 .5 42 .4 41 .3 50 .9 62 .8 58 .4 38 .1 58 .6 44 .5 37 .6 56 .6 21 .7 51 .6 Type of cooking fuel Solid fuel 97 .8 97 .1 88 .8 80 .3 98 .1 98 .5 98 .9 99 .2 81 .0 99 .4 99 .8 80 .6 95 .4 87 .0 Liquid fuel 0 .0 0 .1 0 .0 0 .0 0 .2 0 .0 0 .1 0 .0 9 .5 0 .0 0 .0 0 .0 0 .0 4 .9 Cleaner fuel 2 .2 2 .9 11 .2 19 .7 1 .8 1 .5 1 .0 0 .8 9 .6 0 .6 0 .2 19 .4 4 .6 8 .1 Nutritional status Wasting 15 .8 5 .0 7 .8 4 .7 5 .3 9 .1 3 .7 8 .9 6 .9 18 .1 5 .6 8 .0 6 .8 8 .4 Stunting 34 .5 31 .6 29 .8 17 .9 17 .0 31 .1 28 .8 26 .7 36 .5 43 .3 29 .1 17 .6 26 .7 31 .7 Underweight 25 .7 16 .6 14 .7 10 .8 11 .6 16 .1 10 .3 18 .5 21 .7 36 .2 13 .6 14 .1 15 .7 20 .0 Abbreviations: JSH, Junior High School; SHS, Senior High School. 100 D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 International Health Figure 1. Prevalence of diarrhoea among children under five by country. Figure 2. Prevalence of ARI among children under five by country. 0 t t 9 h h 1 e ( o w ( d w o c t o g A f g w A h a t 3 D I t A c d p c g ( w d p m p p t D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 .94). Likewise, children whose mothers had no formal educa- ion had a 2.15-fold greater odds of experiencing diarrhoea than hose children whose mothers had tertiary education (aOR = 2.15, 5% CI 1.65 to 2.80). Compared with children from rich house- olds, those from poor households were 36% more likely to ave had diarrhoea 2 wk prior to the survey (aOR = 1.36, 95% CI .16 to 1.60). Regarding nutritional status, children who experi- nced wasting (aOR = 1.19, 95% CI 1.05 to 1.35) and underweight aOR = 1.36, 95% CI 1.26 to 1.47) had a 19 and 36% increased dds, respectively, of having diarrhoea compared with children ho were not classified as experiencing wasting and underweight Table 4 ). Also, the model was used to assess predictors of ARI in chil- ren under five in the study. The model revealed that children ho had never been vaccinated had a 68% increased odds f having an ARI than their counterparts who had received hildhood vaccinations (aOR = 1.68, 95% CI 0.53 to 0.87). Fur- hermore, household cooking fuel was found to be a predictor f ARI. Children from households that use solid fuels (e.g. wood, rass, charcoal) had a 36% increased odds of experiencing an RI (aOR = 1.36, 95% CI 1.06 to 1.49) compared with children rom households that used cleaner fuel (e.g. liquefied petroleum as, electricity). Similar to diarrhoea, children who were under- eight had a 10% increased odds of being diagnosed with an RI (aOR = 1.10, 95% CI 1.01 to 1.23). Interestingly, children who ad been diagnosed with diarrhoea at the time of the study had 2.92-fold greater odds of being diagnosed with an ARI than hose children without diarrhoea (aOR = 2.92, 95% CI 2.60 to .29) (Table 4 ). iscussion n this study, researchers investigated the prevalence and de- erminants of diarrhoea and ARIs in children under five in West frica. It was observed that the prevalence of diarrhoea among hildren under five in West Africa was 13.7%. The prevalence of iarrhoea in the current study was marginally below the reported revalence of 15.3% in a study conducted in SSA.8 Contrarily, the urrent prevalence is above that of other countries in different re- ions, such as India (5%),12 Vietnam (11%)13 and Mesoamerica 13%).14 Furthermore, the prevalence of ARI in this study was 15.9%, hich was below the prevalence rate reported by studies con- ucted in Australia (19.9%) and SSA (25.3%).6 , 15 Variations in revalence could be attributable to differences in the environ- ent and infrastructure, such as improved water sources, the resence of improved toilet facilities, and improved waste dis- osal methods.16 This variation could also be influenced by the imeframe for recalling symptoms. The timeframe for recalling 101 D. N. Owusu et al. Table 3. Results of bivariate analysis Diarrhoea among children under five ARI among children under five Characteristics No diarrhoea Diarrhoea χ2 p No ARI ARI χ2 p Overall % % % % Country 470 .12 < 0 .001 1016 .41 < 0 .001 Burkina Faso 85 .1 14 .9 89 .7 10 .3 Benin 89 .5 10 .5 81 .5 18 .5 Cote d’lvoire 81 .5 18 .5 77 .9 22 .1 Ghana 88 .1 11 .9 86 .0 14 .0 Gambia 80 .3 19 .7 79 .1 20 .9 Guinea 85 .4 14 .6 87 .0 13 .0 Liberia 83 .7 16 .3 74 .7 25 .3 Mali 82 .8 17 .2 87 .4 12 .6 Nigeria 87 .2 12 .8 84 .3 15 .7 Niger 85 .6 14 .4 85 .5 14 .5 Serra Leone 92 .8 7 .2 85 .8 14 .2 Senegal 86 .3 13 .7 80 .2 19 .8 Togo 84 .8 15 .2 72 .1 27 .9 Residence 285 .98 < 0 .001 36 .17 0 .01 Urban 88 .5 11 .5 83 .3 16 .7 Rural 85 .1 14 .9 84 .6 15 .4 Child’s age (mo) 1688 .27 < 0 .001 478 .26 < 0 .001 < 6 90 .3 9 .7 85 .5 14 .5 6–11 79 .3 20 .7 78 .7 21 .3 12–35 81 .7 18 .3 81 .3 18 .7 36–47 93 .2 9 .8 84 .6 15 .4 48–59 93 .0 7 .0 88 12 Gender 4 .83 0 .120 0 .132 0 .795 Male 86 .1 13 .9 84 .2 15 .8 Female 86 .5 13 .5 84 .1 15 .9 Mother’s age (y) 390 .43 < 0 .001 64 .46 < 0 .001 15–19 80 .7 19 .3 82 .5 17 .5 20–24 84 .1 15 .9 83 .5 16 .5 25–29 86 .5 13 .5 83 .7 16 .3 30–34 87 .9 12 .1 85 .0 15 .0 35–39 87 .5 12 .5 84 .1 15 .9 40–44 87 .5 12 .5 85 .7 14 .3 45–49 88 .2 11 .8 85 .6 14 .4 Mother’s education 869 .96 < 0 .001 415 .93 < 0 .001 No education 84 .7 15 .3 85 .9 14 .1 Incomplete primary 83 .9 16 .1 80 .1 19 .9 Complete primary 86 .6 13 .4 83 .2 16 .8 Incomplete secondary 87 .0 13 .0 81 .8 18 .2 Complete secondary 91 .6 8 .4 82 .3 17 .7 Tertiary 93 .3 6 .7 83 .3 16 .7 Wealth index 698 .12 < 0 .001 23 .28 0 .22 Poorest 83 .1 16 .9 84 .4 15 .6 Poorer 84 .5 15 .5 84 .2 15 .8 Middle 86 .9 13 .1 84 .5 15 .5 Richer 87 .7 12 .3 84 .1 15 .9 Richest 90 .4 9 .6 83 .1 16 .9 Sanitation 132 .86 < 0 .001 66 .43 < 0 .001 Improved 87 .5 12 .5 83 .2 16 .8 Unimproved 85 .3 14 .7 84 .9 15 .1 102 D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 International Health Table 3. Continued Diarrhoea among children under five ARI among children under five Characteristics No diarrhoea Diarrhoea χ2 p No ARI ARI χ2 p Overall % % % % Water source 133 .03 < 0 .001 0 .5480 0 .7481 Improved 87 .0 13 .0 84 .2 15 .8 Unimproved 84 .6 15 .4 84 16 Breastfeeding 0 .825 0 .5392 16 .04 0 .008 Ever breastfed 86 .3 13 .7 84 .1 15 .9 Never breastfed 86 .9 13 .1 86 .7 13 .3 Vaccination status 7 .604 0 .09 68 .19 < 0 .001 Ever vaccinated 84 .4 15 .6 84 .1 15 .9 Never vaccinated 85 .6 14 .4 87 .6 12 .4 Vitamin A in last 6 mo 3 .865 0 .292 228 .49 < 0 .001 Yes 86 .1 13 .9 82 .6 17 .4 No 86 .5 13 .5 85 .6 14 .4 Type of cooking fuel 662 .20 < 0 .001 34 .21 0 .07 Liquid fuel 85 .4 14 .6 84 .3 15 .7 Solid fuel 94 .6 5 .4 84 .3 15 .7 Cleaner fuel 91 .1 8 .9 82 .1 17 .9 Nutritional status Wasting 262 .05 < 0 .001 10 .14 0 .02 Yes 76 .1 20 .9 81 .8 18 .2 No 86 .7 13 .3 83 .5 16 .5 Stunting 221 .78 < 0 .001 14 .13 0 .01 Yes 83 .2 16 .8 84 .1 15 .9 No 87 .3 12 .7 82 .9 17 .1 Underweight 489 .71 < 0 .001 1 .676 0 .395 Yes 80 .2 19 .8 82 .9 17 .1 No 87 .4 12 .6 83 .4 16 .6 s f s 2 h E t v c r t t a i t c s c e t b i f U t p b m o d c p f i h w h t P s t T i D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 ymptoms in this study was 2 wk before the survey, whereas or the Australian study it was 4 wk. Finally, the current study howed that about 4.4% of children had both diarrhoea and ARI wk prior to the survey. This prevalence is twice the prevalence of aving both diarrhoea and ARI reported by a study conducted in ast Africa.8 The comorbid patterns of these conditions highlight he seriousness with which the individual conditions must be pre- ented and treated to avert the potential negative impact of the oncurrent conditions in children under five. Children aged < 2 y (i.e. 6–23 mo) had a significantly higher isk of diarrhoea and ARIs than older children. This could be at- ributable to the lower immunity in children under five. Although he risk factors of ARI and diarrhoea are complex and cannot be ttributed solely to the child’s age, it appears that this is a signif- cant contributor. This finding is consistent with previous studies hat reported a higher likelihood of ARIs and diarrhoea among hildren aged < 36 mo.17 , 18 Furthermore, approximately one-half of the mothers in the urvey had no formal education. This finding is not surprising be- ause a UNESCO assessment indicated that Africa has the great- st rates of school exclusion, particularly among women.19 Addi- ionally, it has been demonstrated that the increased economic urden in subregions of Africa translates into families’ incapac- ty to provide formal education for their children. Also, cultural actors may explain the high prevalence of uneducated women. ntil recently, most women in Africa’s subregions were expected o mainly undertake domestic chores, that is, women were sup- osed to stay at home and handle all the domestic responsi- ilities. The same cultural practices in Africa encouraged early arriages, depriving women of formal education.20 , 21 The level f education of mothers was linked to a higher prevalence of iarrhoea in their children. Diarrhoea was twice as common in hildren whose mothers had no formal education. This is not sur- rising considering that education enables women to be well in- ormed about how to find and utilise appropriate child health nformation.22 , 23 The current study also found that children in poor households ad a higher probability of experiencing diarrhoea compared ith children in rich households. In general, children in poor ouseholds may face a level of social and health inequalities hat can predispose them to preventable health conditions. oor households are likely to experience inconsistent nutritional upplies and live in unhygienic neighbourhoods, which increases he risk of exposure to the microbial agents that cause diarrhoea. his finding conforms with those of previous studies conducted n Kenya and Ethiopia.8 , 24 103 D. N. Owusu et al. Table 4. Multivariate logistic regression estimates of predictors of diarrhoea and ARI among children under five in West Africa Characteristics Diarrhoea among children under five ARI among children under five Unadjusted model Adjusted model Unadjusted model Adjusted model Residence cOR [CI] p aOR [CI] p cOR [CI] p aOR [CI] p Urban 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Rural 1.34 [1.06, 1.70] 0 .014 0.92 [0.77, 1.10] 0 .364 0.90 [0.72, 1.14] 0 .425 0.90 [0.71, 1.15 0 .436 Child’s age (mo) 6–23 1.82 [1.66, 1.96] < 0 .001 1.57 [1.30, 1.90] < 0 .001 1.32 [1.24, 1.40] < 0 .001 1.14 [0.96, 1.34] 0 .118 24–59 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Gender Male 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Female 0.96 [0.92, 1.00] 0 .075 0.99 [0.94, 1.04] 0 .931 1.00 [0 .96, 1.05] 0 .806 1.01 [0.95, 1.07] 0 .679 Mother’s age (y) 15–24 1.00 [reference] 1.00 [reference] 1 [reference] 25–29 0.78 [0.73, 0.83] < 0 .001 0.92 [0.84, 1.00] 0 .078 0.97 [0.90, 1.04] 0 .479 1.07 [0.90, 1.27] 0 .391 30–39 0.70 [0.64, 0.76] < 0 .001 0.84 [0.77, 0.91] < 0 .001 0.90 [0.83, 0.97] 0 .010 1.05 [0.89, 1.23] 0 .538 ≥40 0.70 [0.64, 0.77] < 0 .001 0.83 [0.74, 0.94] 0 .003 0.83 [0.75, 0.91] < 0 .001 0.95 [0.76, 1.18] 0 .672 Mother’s education No education 2.50 [1.93, 3.23] < 0 .001 2.15 [1.65, 2.80] < 0 .001 0.87 [0.69, 1.10] 0 .258 0.79 [0.53, 1.19] 0 .271 Primary/JHS 2.07 [1.70, 2.52] < 0 .001 1.88 [1.49, 2.38] < 0 .001 1.07 [0.89, 1.28] 0 .458 1.01 [0.70, 1.46] 0 .923 SHS 1.25 [1.02, 1.53] 0 .028 1.08 [0.80, 1.46] 0 .596 1.07 [0.94, 1.22] 0 .280 1.03 [0.76, 1.41] 0 .820 Tertiary 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Wealth index Poor 1.56 [1.25, 1.94] < 0 .001 1.36 [1.16, 1.60] < 0 .001 0.95 [0.77, 1.17] 0 .636 0.98 [0.74, 1.32] 0 .940 Middle 1.21 [1.04, 1.41] 0 .011 1.13 [0.99, 1.29] 0 .054 0.93 [0.81, 1.07] 0 .363 0.96 [0.76, 1.21] 0 .747 Rich 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Sanitation Improved 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Unimproved 1.20 [1.04, 1.39] 0 .012 0.89 [0.79, 1.00] 0 .068 0.88 [0.77, 1.00] 0 .06 0.93 [0.81, 1.06] 0 .287 Water source Improved 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Unimproved 1.21 [1.07, 1.37] 0 .002 1.01 [0.91, 1.12] 0 .737 1.01 [0.87, 1.17] 0 .871 1.07 [0.96, 1.19] 0 .173 Breastfeeding Ever breastfed 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Never breastfed 0.95 [0.77, 1.17] 0 .646 1.05 [0.70, 1.59] 0 .790 0.81 [0.66, 0.98] 0 .039 1.05 [0.72, 1.54] 0 .773 Vaccination status Ever vaccinated 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Never vaccinated 0.912 [0.76, 1.08] 0 .287 0.83 [0.68, 1.99] 0 .067 1.75 [0.61, 0.93] 0 .008 1.68 [0.53, 0.87] 0 .002 Vitamin A in last 6 mo Yes 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] No 0.96 [0.83, 1.13] 0 .689 0.96 [0.81, 1.15] 0 .733 0.79 [0.68, 0.94] 0 .006 0.98 [0.83, 1.16] 0 .877 Type of cooking fuel Solid fuel 1.87 [1.68, 2.09] < 0 .001 1.36 [1.06, 1.49] 0 .007 Liquid fuel 0.85 [0.62, 1.16] 0 .325 0.85 [0.58, 1.23] 0 .391 Cleaner fuel 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Nutritional status Wasting Yes 1.71 [1.51, 1.95] < 0 .001 1.19 [1.05, 1.35] 0 .005 1.11 [0.99, 1.25] 0 .057 1.03 [0.92, 1.16] 0 .505 No 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Stunting Yes 1.39 [1.26, 1.53] < 0 .001 1.08 [0.99, 1.19] 0 .07 0.92 [0.82, 1.03] 0.91 [0.82, 1.02] 0 .131 No 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Underweight Yes 1.712 [1.56, 1.86] < 0 .001 1.36 [1.26, 1.47] < 0 .001 1.03 [0.92, 1.15] 0 .584 1.10 [1.01, 1.23] 0 .05 No 1.00 [reference] 1.00 [reference] 1.00 [reference] 1.00 [reference] Diarrhoea No 1.00 [reference] 1.00 [reference] Yes 3.16 [2.85, 3.46] < 0 .001 2.92 [2.60, 3.29] < 0 .001 Abbreviations: aOR, adjusted OR; cOR, crude OR; JSH, Junior High School; SHS, Senior High School. 104 D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 International Health n t f t r r o i m h T t p a f ( i H a i a m e T p d t h d t o T A t n c m fi o a p i v I h c i S T a W q s d r f n t C T t 1 p t d e e t w b a v a g d s A H o t f m A m F C E t o b t D s i R D ow nloaded from https://academ ic.oup.com /inthealth/article/16/1/97/7210800 by U niversity of G hana user on 01 February 2024 Also, the current study showed that children who were mal- ourished (i.e. experiencing wasting and underweight) were likely o experience diarrhoea and ARIs. The plausible explanation or this is that malnutrition among children generally denies hem the essential micronutrients needed to combat childhood- elated diseases such as diarrhoea. Malnutrition enables diar- hoeal infections to occur more frequently and for longer peri- ds, with about a 38% increase in frequency and a 73% increase n length accounting for a doubling of the diarrhoea burden in alnourished children.14 The study also found that the use of solid fuel for cooking in ouseholds was associated with a higher odds of ARIs in children. his finding is congruent with other studies that have revealed hat solid fuel produces a lot of smoke which causes household air ollution (HAP) and renders children vulnerable to a higher prob- bility of respiratory infection.25 , 26 HAP from the burning of solid uel predisposes the entire home to elevated carbon monoxide CO) and particulate matter (PM2.5 ) levels, which can lead to an ncreased risk of respiratory diseases. The detrimental effects of AP appear to disproportionately affect vulnerable groups such s children. Air pollution from the burning of solid fuels primar- ly affects the respiratory system, resulting in a variety of acute nd chronic symptoms. Respiratory side effects might range from inor symptoms and changes to life-threatening illnesses and ven death. Households may benefit from improved cooking fuel. hose that cannot make the transition to clean fuel can use im- roved cooking stoves, which are associated with low HAP, to re- uce the risk of respiratory diseases.27 Interestingly, the current study observed an association be- ween diarrhoea and ARI. Children under five in West Africa who ad diarrhoea were more likely to have an ARI than those chil- ren who had no diarrhoea. One plausible explanation for this is hat diarrhoea appears to dehydrate children and deprive them f important micronutrients that aid in immune strengthening. hus, impaired immunity predisposes them to infections such as RIs. It is also plausible that both ARI and diarrhoea are symp- oms of another underlying condition. Given the cross-sectional ature of the study, we were unable to describe the order of oc- urrence of the two conditions and no causal inference can be ade regarding the association between diarrhoea and ARI. This nding is consistent with earlier research concerning the impact f diarrhoea on children under five.5,1 4 Moreover, the study showed that children who have never had vaccination were at a higher risk of experiencing an ARI com- ared with children who had received vaccinations. This finding s congruent with other studies in Asia and Africa that have re- ealed an association between vaccination status and ARI.28 , 29 n those studies, the association was plausible because vaccines elp children to build immunity against infections. Pneumococ- al vaccines, for example, help to reduce the risk of respiratory nfections. trengths and limitations he strengths of the current study relate to the use of a large mount of nationally representative data from 13 countries in est Africa. The large dataset implies that the study is ade- uately powered. We also adjusted for sampling weight, primary ampling units and strata to ensure correct estimates of stan- ard errors. However, for the 13 countries studied here, the most ecent DHS was used, and several of these were conducted in dif- erent years. Moreover, the DHS is cross-sectional in nature, hence o causal inferences can be made from the observed associa- ions. onclusions he current study sought to investigate the prevalence and de- erminants of diarrhoea and ARIs in children under five across 3 different countries in the West African subregion. The overall revalence of diarrhoea and ARI was 13.7% and 15.9%, respec- ively. The comorbid burden of the two conditions was 4.4%. Chil- ren aged < 2 y, mothers aged < 30 y, mothers without a formal ducation, poor households and poor nutritional status (experi- ncing wasting and underweight), were the independent predic- ors of diarrhoea. The independent predictors of ARI were children ith no childhood vaccinations, use of solid fuel in the household, eing underweight and having diarrhoea. These findings imply need for holistic public health interventions, such as increased accination coverage, community-based nutritional programmes nd campaigns promoting the use of cleaner cooking fuel, tar- eted at high-risk subgroups in the population to reduce the bur- en and adverse effects of diarrhoea and ARIs in the West Africa ubregion. uthors’ contributions: DNO, HOD, DD and YA designed the study; DNO, OD and DD wrote the introduction; DNO and YA wrote the methodol- gy, and performed the data analysis and interpretation. DNO drafted he manuscript; DNO, HOD, DD and YA critically revised the manuscript or intellectual content. All the authors read and approved the final anuscript. DNO and HOD are the guarantors of the paper. cknowledgements: We would like to thank the DHS programme for per- itting us to use the datasets. unding: None. ompeting interests: The authors declare no conflicts of interest. thical approval: The procedures/protocols used to collect data during he DHS were reviewed and approved by the Institutional Review Board f ICF International. Informed consent from participants was obtained y trained enumerators during the survey. No additional consent was ob- ained for the analysis of de-identified secondary data. ata availability: Data used for the study are freely available after a imple online request at https://dhsprogram.com/data/dataset_admin/ ndex.cfm . eferences 1 WHO. Estimates Developed by the United Nations Inter-Agency Group for Child Mortality Estimation; 2022. 2 Troeger C, Forouzanfar M, Rao PC, et al. 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Acute lower respiratory trac t infec tions and associated fac tors among under-five children vis- iting Wolaita Sodo University Teaching and Referral Hospital, Wolaita Sodo, Ethiopia. BMC Pediatr. 2021;21:1–8. https://creativecommons.org/licenses/by/4.0/ Introduction Methods Overview of the demographic and health survey Study population Study variables Data analysis Results Sample characteristics Prevalence of diarrhoea among children under five in West Africa Prevalence of ARI among children under five in West Africa Results of bivariate analysis Multivariable logistic regression estimates of predictors of diarrhoea and ARI among children under five in West Africa Discussion Strengths and limitations Conclusions References