Functional Disability and Social Support in Older Patients attending Chief Tony Anenih Geriatrics Center (CTAGC) University College Hospital, Ibadan Southwestern, Nigeria

BACKGROUND

Beyond clinical evaluation, additional significant areas of well-being for older people include the emotional, social, material, and functional domains.

OBJECTIVES

The study assessed the functional status and its relationship with social support of older patients attending the Geriatric Centre, UCH.

METHODOLOGY

A cross-sectional study of 396 randomly selected patients aged 65 years and above was undertaken to assess their functional status (by scoring their basic activities of daily living using the Barthel index) and social support (using the Multidimensional scale of perceived social support). An interviewer-administered questionnaire was used to obtain the socio-demographic data, anthropometric measurements, and morbidities of each patient. Data collected was analysed using the Statistical Package for Social Sciences (SPSS) version 20. The level of significance of analysis was set at p ≤0.05.

RESULTS

Participants in the study had a male-to-female ratio of 1:1.6, and their mean age (SD) was 73.2 ± 6.3 years. Functional dependency was seen in 87.4% of cases. Majority of older patients (81.1%) expressed a moderate perception of social support. The Most common morbidities among the responders were osteoarthritis, cataracts, and hypertension. Functional dependency was found to increase with an increase in family and romantic partner social support, high educational levels, and increased age. {adjusted odds ratio (AOR):1.05;95%confidence interval [CI]:1.00-1.11. (P=0.049) The odds of being functionally dependent were higher for respondents who received at least 30,000 naira ($100) in financial support from their children (AOR:2.24; 95% CI:1.06-4.77) (P=0.022).

CONCLUSION

This study showed that functional dependency worsened with increased social support in older patients. The results indicated the need for a multi-factorial evaluation of functional dependence in older patients.

Evaluating the performance of different Bayesian count models in modelling childhood vaccine uptake among children aged 12-23 months in Nigeria

BACKGROUND

Choosing appropriate models for count health outcomes remains a challenge to public health researchers and the validity of the findings thereof. For count data, the mean-variance relationship and proportion of zeros is a major determinant of model choice. This study aims to compare and identify the best Bayesian count modelling technique for the number of childhood vaccine uptake in Nigeria.

METHODS

We explored the performances of Poisson, negative binomial and their zero-inflated forms in the Bayesian framework using cross-sectional data pooled from the Nigeria Demographic and Health Survey conducted between 2003 and 2018. In multivariable analysis, these Bayesian models were used to identify factors associated with the number of vaccine uptake among children. Model selection was based on the -2 Log-Likelihood (-2 Log LL), Leave-One-Out Cross-Validation Information Criterion (LOOIC) and Watanabe-Akaike/Widely Applicable Information Criterion (WAIC).

RESULTS

Exploratory analysis showed the presence of excess zeros and overdispersion with a mean of 4.36 and a variance of 12.86. Observably, there was a significant increase in vaccine uptake over time. Significant factors included the mother's age, level of education, religion, occupation, desire for last-child, place of delivery, exposure to media, birth order of the child, wealth status, number of antenatal care visits, postnatal attendance, healthcare decision maker, community poverty, community illiteracy, community unemployment, rural proportion and number of health facilities per 100,000. The zero-inflated negative binomial model was best fit with -2Log LL of -27171.47, LOOIC of 54464.2, and WAIC of 54588.0.

CONCLUSION

The Bayesian zero-inflated negative binomial model was most appropriate to identify factors associated with the number of childhood vaccines received in Nigeria due to the presence of excess zeros and overdispersion. Improving vaccine uptake by addressing the associated risk factors should be promptly embraced.

Prevalence of cardio-metabolic multi-morbidity and associated risk factors in a population-based sample of South Africans

Objectives

Cardio-metabolic multi-morbidity (CM), the co-existence of two or more cardio-metabolic disorders in the same person, is rapidly increasing. We examined the prevalence and risk factors associated with CM in a population-based sample of South African adults.

Study design

Data were analyzed on individuals aged ≥15 years from the South African National Health and Nutrition Examination Survey (SANHANES), a cross sectional population-based survey conducted in 2011-2012.

Methods

CM was defined as having ≥2 of hypertension, diabetes, stroke and angina. Multivariable logistic regression was used to investigate the sociodemographic and modifiable risk factors associated with CM.

Results

Of the 3832 individuals analyzed, the mean age was 40.8 years (S.D. 18.3), 64.5% were female and 18% were ≥60 years. The prevalence of CM was 10.5%. The most prevalent CM cluster was hypertension and diabetes (7.3%), followed by hypertension and angina (2.6%) and hypertension and stroke (1.9%). Of the individuals with diabetes, nearly three quarters had multi-morbidity from co-occurring hypertension, angina and/or stroke and of those with hypertension, 30% had co-occurring diabetes, angina and/or stroke. Age (30-44 years Adjusted Odds Ratio (AOR) = 2.68, 95% CI: 1.15-6.26), 45-59 years AOR = 16.32 (7.38-36.06), 60-74 years AOR = 40.14 (17.86-90.19), and ≥75 years AOR = 49.54 (19.25-127.50) compared with 15-29 years); Indian ethnicity (AOR = 2.58 (1.1-6.04) compared with black African ethnicity), overweight (AOR = 2.73 (1.84-4.07)) and obesity (AOR = 4.20 (2.75-6.40)) compared with normal or underweight) were associated with increased odds of CM.

Conclusions

A tenth of South Africans have two or more cardio-metabolic conditions. The findings call for immediate prioritization of prevention, screening and management of cardio-metabolic conditions and their risk factors to avert large scale health care costs and adverse health outcomes associated with multi-morbidity.

Approximation of the Cox survival regression model by MCMC Bayesian Hierarchical Poisson modelling of factors associated with childhood mortality in Nigeria

The need for more pragmatic approaches to achieve sustainable development goal on childhood mortality reduction necessitated this study. Simultaneous study of the influence of where the children live and the censoring nature of children survival data is scarce. We identified the compositional and contextual factors associated with under-five (U5M) and infant (INM) mortality in Nigeria from 5 MCMC Bayesian hierarchical Poisson regression models as approximations of the Cox survival regression model. The 2018 DHS data of 33,924 under-five children were used. Life table techniques and the Mlwin 3.05 module for the analysis of hierarchical data were implemented in Stata Version 16. The overall INM rate (INMR) was 70 per 1000 livebirths compared with U5M rate (U5MR) of 131 per 1000 livebirth. The INMR was lowest in Ogun (17 per 1000 live births) and highest in Kaduna (106), Gombe (112) and Kebbi (116) while the lowest U5MR was found in Ogun (29) and highest in Jigawa (212) and Kebbi (248). The risks of INM and U5M were highest among children with none/low maternal education, multiple births, low birthweight, short birth interval, poorer households, when spouses decide on healthcare access, having a big problem getting to a healthcare facility, high community illiteracy level, and from states with a high proportion of the rural population in the fully adjusted model. Compared with the null model, 81% vs 13% and 59% vs 35% of the total variation in INM and U5M were explained by the state- and neighbourhood-level factors respectively. Infant- and under-five mortality in Nigeria is influenced by compositional and contextual factors. The Bayesian hierarchical Poisson regression model used in estimating the factors associated with childhood deaths in Nigeria fitted the survival data.

Mind the gap: what explains the rural-nonrural inequality in diarrhoea among under-five children in low and medium-income countries? A decomposition analysis

BACKGROUND

Diarrhoea poses serious health problems among under-five children (U5C) in Low-and Medium-Income Countries (LMIC) with a higher prevalence in rural areas. A gap exists in knowledge on factors driving rural-non-rural inequalities in diarrhoea development among U5C in LMIC. This study investigates the magnitude of rural-non-rural inequalities in diarrhoea and the roles of individual-level and neighbourhood-level factors in explaining these inequalities.

METHODS

Data of 796,150 U5C, from 63,378 neighbourhoods across 57 LMIC from the most recent Demographic and Health Survey (2010-2018) was analysed. The outcome variable was the recent experience of diarrhoea while independent variables consist of the individual- and neighbourhood-level factors. Data were analysed using multivariable Fairlie decomposition at p < 0.05 in Stata Version 16 while visualization was implemented in R Statistical Package.

RESULTS

Two-thirds (68.0%) of the children are from rural areas. The overall prevalence of diarrhoea was 14.2, 14.6% vs 13.4% among rural and non-rural children respectively (p < 0.001). From the analysis, the following 20 countries showed a statistically significant pro-rural inequalities with higher odds of diarrhoea in rural areas than in nonrural areas at 5% alpha level: Albania (OR = 1.769; p = 0.001), Benin (OR = 1.209; p = 0.002), Burundi (OR = 1.399; p < 0.001), Cambodia (OR = 1.201; p < 0.031), Cameroon (OR = 1.377; p < 0.001), Comoros (OR = 1.266; p = 0.029), Egypt (OR = 1.331; p < 0.001), Honduras (OR = 1.127; p = 0.027), India (OR = 1.059; p < 0.001), Indonesia (OR = 1.219; p < 0.001), Liberia (OR = 1.158; p = 0.017), Mali (OR = 1.240; p = 0.001), Myanmar (OR = 1.422; p = 0.004), Namibia (OR = 1.451; p < 0.001), Nigeria (OR = 1.492; p < 0.001), Rwanda (OR = 1.261; p = 0.010), South Africa (OR = 1.420; p = 0.002), Togo (OR = 1.729; p < 0.001), Uganda (OR = 1.214; p < 0.001), and Yemen (OR = 1.249; p < 0.001); and pro-non-rural inequalities in 9 countries. Variations exist in factors associated with pro-rural inequalities across the 20 countries. Overall main contributors to pro-rural inequality were neighbourhood socioeconomic status, household wealth status, media access, toilet types, maternal age and education.

CONCLUSIONS

The gaps in the odds of diarrhoea among rural children than nonrural children were explained by individual-level and neighbourhood-level factors. Sustainable intervention measures that are tailored to country-specific needs could offer a better approach to closing rural-non-rural gaps in having diarrhoea among U5C in LMIC.

Decomposition analysis of the compositional and contextual factors associated with poor-non-poor inequality in diarrhoea among under-five children in low- and middle-income countries

OBJECTIVES

The aim of the study was to assess the magnitude of wealth inequalities in the development of diarrhoea among under-five children in low- and middle-income countries (LMICs) and to identify and quantify contextual and compositional factors' contribution to the inequalities.

DESIGN

This is a cross-sectional study.

METHODS

We used cross-sectional data from 57 Demographic and Health Surveys conducted between 2010 and 2018 in LMICs. Descriptive statistics were used to understand the gap in having diarrhoea between the children from poor and non-poor households and across the selected covariates using Fairlie decomposition techniques with multivariable binary logistic regressions at P = 0.05.

RESULTS

Of the 57 countries, we found a statistically significant pro-poor odds ratio in only 29 countries, 7 countries showed pro-non-poor inequality and others showed no statistically significant inequality. Among the countries with statistically significant pro-poor inequality, the risk difference was largest in Cameroon (94.61/1000), whereas the largest pro-non-poor risk difference in diarrhoea was widest in Timor-Leste (-41.80/1000). Important factors responsible for pro-poor inequality varied across countries. The largest contributors to the pro-poor inequalities in having diarrhoea are maternal education, access to media, neighbourhood socio-economic status, place of residence, birth order and maternal age.

CONCLUSION

Diarrhoea remains a major challenge in most LMICs, with a wide range of pro-poor inequalities. These disparities were explained by both compositional and contextual factors, which varied widely across the countries. Thus, multifaceted geographically specific economic alleviation intervention may prove to be a potent approach for addressing the poor and non-poor differentials in the risk of diarrhoea with policies tailored to country-specific risk factors. There is a need for further investigation of factors that drive pro-non-poor inequalities found in 9 of the LMICs.

Demystifying the factors associated with rural-urban gaps in severe acute malnutrition among under-five children in low- and middle-income countries: a decomposition analysis

What explains the underlying causes of rural-urban differentials in severe acute malnutrition (SAM) among under-five children is poorly exploited, operationalized, studied and understood in low- and middle-income countries (LMIC). We decomposed the rural-urban inequalities in the associated factors of SAM while controlling for individual, household, and neighbourhood factors using datasets from successive demographic and health survey conducted between 2010 and 2018 in 51 LMIC. The data consisted of 532,680 under-five children nested within 55,823 neighbourhoods across the 51 countries. We applied the Blinder-Oaxaca decomposition technique to quantify the contribution of various associated factors to the observed rural-urban disparities in SAM. In all, 69% of the children lived in rural areas, ranging from 16% in Gabon to 81% in Chad. The overall prevalence of SAM among rural children was 4.8% compared with 4.2% among urban children. SAM prevalence in rural areas was highest in Timor-Leste (11.1%) while the highest urban prevalence was in Honduras (8.5%). Nine countries had statistically significant pro-rural (significantly higher odds of SAM in rural areas) inequality while only Tajikistan and Malawi showed statistically significant pro-urban inequality (p < 0.05). Overall, neighbourhood socioeconomic status, wealth index, toilet types and sources of drinking water were the most significant contributors to pro-rural inequalities. Other contributors to the pro-rural inequalities are birth weight, maternal age and maternal education. Pro-urban inequalities were mostly affected by neighbourhood socioeconomic status and wealth index. Having SAM among under-five children was explained by the individual-, household- and neighbourhood-level factors. However, we found variations in the contributions of these factors. The rural-urban dichotomy in the prevalence of SAM was generally significant with higher odds found in the rural areas. Our findings suggest the need for urgent intervention on child nutrition in the rural areas of most LMIC.

Decomposing the educational inequalities in the factors associated with severe acute malnutrition among under-five children in low- and middle-income countries

BACKGROUND

Low- and Middle-Income Countries (LMIC) have remained plagued with the burden of severe acute malnutrition (SAM). The decomposition of the educational inequalities in SAM across individual, household and neighbourhood characteristics in LMIC has not been explored. This study aims to decompose educational-related inequalities in the development of SAM among under-five children in LMIC and identify the risk factors that contribute to the inequalities.

METHODS

We pooled successive secondary data from the Demographic and Health Survey conducted between 2010 and 2018 in 51 LMIC. We analysed data of 532,680 under-five children nested within 55,823 neighbourhoods. Severe acute malnutrition was the outcome variable while the literacy status of mothers was the main exposure variable. The explanatory variables cut across the individual-, household- and neighbourhood-level factors of the mother-child pair. Oaxaca-Blinder decomposition method was used at p = 0.05.

RESULTS

The proportion of children whose mothers were not educated ranged from 0.1% in Armenia and Kyrgyz Republic to as much as 86.1% in Niger. The overall prevalence of SAM in the group of children whose mothers had no education was 5.8% compared with 4.2% among those whose mothers were educated, this varied within each country. Fourteen countries (Cameroon(p < 0.001), Chad(p < 0.001), Comoro(p = 0.047), Burkina Faso(p < 0.001), Ethiopia(p < 0.001), India(p < 0.001), Kenya(p < 0.001), Mozambique(p = 0.012), Namibia(p = 0.001), Nigeria(p < 0.001), Pakistan(p < 0.001), Senegal(p = 0.003), Togo(p = 0.013), and Timor Leste(p < 0.001) had statistically significant pro-illiterate inequality while no country showed statistically significant pro-literate inequality. We found significant differences in SAM prevalence across child's age (p < 0.001), child's sex(p < 0.001), maternal age(p = 0.001), household wealth quintile(p = 0.001), mother's access to media(p = 0.001), birth weight(p < 0.001) and neighbourhood socioeconomic status disadvantage(p < 0.001). On the average, neighbourhood socioeconomic status disadvantage, location of residence were the most important factors in most countries. Other contributors to the explanation of educational inequalities are birth weight, maternal age and toilet type.

CONCLUSIONS

SAM is prevalent in most LMIC with wide educational inequalities explained by individual, household and community-level factors. Promotion of women education should be strengthened as better education among women will close the gaps and reduce the burden of SAM generally. We recommend further studies of other determinate causes of inequalities in severe acute malnutrition in LMIC.

Survival analysis and prognostic factors of time to first domestic violence after marriage among Nigeria, Kenya, and Mozambique women

OBJECTIVES

How soon an ever-married woman falls a victim of domestic violence after marriage is not documented in Africa. This study sought to assess the timing of first domestic violence (FDV) against women after marriage and determined the factors associated with the timings in Nigeria, Kenya, and Mozambique.

STUDY DESIGN

This is a cross-sectional study.

METHODS

Data of 29,793 ever-married women of reproductive age consisting of 21,564, 4237 and 3992 from Demographic and Health Survey conducted in Nigeria (2013), Kenya (2014) and Mozambique (2011), respectively, were used. The timing of FDV was the time interval between marriage date and date of the FDV for those with reported violence but censored as the time interval between marriage date and the survey date for those without domestic violence. Survival analysis techniques were used to assess the timing and the factors influencing the timing at (P = 0.05).

RESULTS

The lifetime prevalence of domestic violence among the ever-married women in Nigeria, Kenya and Mozambique was 15.4%, 39.0% and 31.0%, respectively. The overall median time to FDV was 3 years. The risk of FDV was twice higher in Kenya (adjusted hazard ratio (aHR) = 1.934; 95% confidence interval (CI): 1.729-2.132) and 15% higher in Mozambique (aHR = 1.156; 95% CI: 1.156-1.223) than in Nigeria. The hazard of domestic violence was significantly higher among separated/divorced women across the three countries (aHR = 1.326; 95% CI: 1.237-1.801). Other factors associated with the timing of FDV against women were respondents' education, age at first marriage, region and location of residence, religion, ethnicity, employment status, wealth quintile, spouse consuming alcohol and husbands' educational attainment.

CONCLUSIONS

Domestic violence against married women by their intimate partners is prevalent across Mozambique, Nigeria and Kenya, with earlier occurrences in Kenya and Mozambique. Age at first marriage, education factors, religion, ethnicity and region of residence in each country affected the timing of the first incidence of domestic violence.

Modelling self-assessed vulnerability to HIV and its associated factors in a HIV-burdened country

BACKGROUND

Globally, individuals' self-assessment of vulnerability to HIV infection is important to maintain safer sexual behaviour and reduce risky behaviours. However, determinants of self-perceived risk of HIV infection are not well documented and differ. We assessed the level of self-perceived vulnerability to HIV infection in Nigeria and also identified its risk factors.

METHODS

We explored a recent nationally representative data with self-reported vulnerability ('high', 'low' and 'no risk at all') to HIV infection as the outcome of interest. Data were weighted and association between the outcomes and the risk factors determined. We used simple ordered logit regression to model relationship between the outcome variable and risk factors, and controlled for the significant variables in multiple ordered logistic regression at 5% significance level.

RESULTS

About 74% had good knowledge of HIV transmission and 6% had experienced STI recently. The likelihood of assessing oneself as having 'no risk at all' was 50% and for 'high chances' was 1.6%. Self-perceived high risk of HIV was higher among those who recently experienced STI (5.6%) than those who did not (1.7%), and also higher among those who recently engaged in transactional sex and had multiple sexual partners. The odds of good knowledge of HIV transmission on high self-perceived vulnerability to HIV was 19% higher than poor knowledge (OR = 1.19, 95% CI: 1.12-1.27). Also, respondents who recently had multiple sexual partners were 72% (OR = 1.72, 95% CI: 1.60-1.86) more likely to report self as having high risk. Younger respondents aged 14-19 years had higher odds of 41% (OR = 1.41, 95% CI: 1.29-1.55) to perceive self as having high vulnerability to HIV than older respondents.

CONCLUSION

High vulnerability to HIV infection was reported among younger respondents, those with history of STIS and those who engage in multiple sexual relations. Despite high level of risky sexual behaviour and good knowledge of HIV transmission and prevention found in this study, self-perceived vulnerability to HIV generally is low. For the low perception found in this study to translate to low chance of HIV infection, there is need for all stakeholders to embark on risk reduction initiatives through sexual education that would minimise risky sexual practices and ensuring availability and affordability of HIV prevention methods.