Geoadditive Latent Variable Modelling of Child Morbidity and Malnutrition in Nigeria

Investigating the impact of important risk factors and geographical location on child morbidity and malnutrition is of high relevance for developing countries. Previous research has usually carried out separate regression analyses for certain diseases or types of malnutrition, neglecting possible association between them. Based on data from the Nigeria Demographic and Health Survey of 2003, we apply recently developed geoadditive latent variable models, taking cough, fever and diarrhea as well as stunting and underweight as observable indicators for the latent variables morbidity and mortality. This allows to study the common impact of risk factors and geographical location on these latent variables, thereby taking account of association within a joint model. Our analysis identifies socio-economic and public health factors, nonlinear effects of age and other continuous covariates as well as spatial effects jointly influencing morbidity and malnutrition

Mapping geographic inequalities in oral rehydration therapy coverage in low- and middle-income countries, 2000–17

Background Oral rehydration solution (ORS) is a form of oral rehydration therapy (ORT) for diarrhoea that has the potential to drastically reduce child mortality; yet, according to UNICEF estimates, less than half of children younger than 5 years with diarrhoea in low-income and middle-income countries (LMICs) received ORS in 2016. A variety of recommended home fluids (RHF) exist as alternative forms of ORT; however, it is unclear whether RHF prevent child mortality. Previous studies have shown considerable variation between countries in ORS and RHF use, but subnational variation is unknown. This study aims to produce high-resolution geospatial estimates of relative and absolute coverage of ORS, RHF, and ORT (use of either ORS or RHF) in LMICs. Methods We used a Bayesian geostatistical model including 15 spatial covariates and data from 385 household surveys across 94 LMICs to estimate annual proportions of children younger than 5 years of age with diarrhoea who received ORS or RHF (or both) on continuous continent-wide surfaces in 2000–17, and aggregated results to policy-relevant administrative units. Additionally, we analysed geographical inequality in coverage across administrative units and estimated the number of diarrhoeal deaths averted by increased coverage over the study period. Uncertainty in the mean coverage estimates was calculated by taking 250 draws from the posterior joint distribution of the model and creating uncertainty intervals (UIs) with the 2·5th and 97·5th percentiles of those 250 draws. Findings While ORS use among children with diarrhoea increased in some countries from 2000 to 2017, coverage remained below 50% in the majority (62·6%; 12 417 of 19 823) of second administrative-level units and an estimated 6 519 000 children (95% UI 5 254 000–7 733 000) with diarrhoea were not treated with any form of ORT in 2017. Increases in ORS use corresponded with declines in RHF in many locations, resulting in relatively constant overall ORT coverage from 2000 to 2017. Although ORS was uniformly distributed subnationally in some countries, within-country geographical inequalities persisted in others; 11 countries had at least a 50% difference in one of their units compared with the country mean. Increases in ORS use over time were correlated with declines in RHF use and in diarrhoeal mortality in many locations, and an estimated 52 230 diarrhoeal deaths (36 910–68 860) were averted by scaling up of ORS coverage between 2000 and 2017. Finally, we identified key subnational areas in Colombia, Nigeria, and Sudan as examples of where diarrhoeal mortality remains higher than average, while ORS coverage remains lower than average. Interpretation To our knowledge, this study is the first to produce and map subnational estimates of ORS, RHF, and ORT coverage and attributable child diarrhoeal deaths across LMICs from 2000 to 2017, allowing for tracking progress over time. Our novel results, combined with detailed subnational estimates of diarrhoeal morbidity and mortality, can support subnational needs assessments aimed at furthering policy makers' understanding of within-country disparities. Over 50 years after the discovery that led to this simple, cheap, and life-saving therapy, large gains in reducing mortality could still be made by reducing geographical inequalities in ORS coverage

Childhood malnutrition in Egypt using geoadditive gaussian and latent variable models

Major progress has been made over the last 30 years in reducing the prevalence of malnutrition amongst children less than 5 years of age in developing countries. However, approximately 27% of children under the age of 5 in these countries are still malnourished. This work focuses on the childhood malnutrition in one of the biggest developing countries, Egypt. This study examined the association between bio-demographic and socioeconomic determinants and the malnutrition problem in children less than 5 years of age using the 2003 Demographic and Health survey data for Egypt. In the first step, we use separate geoadditive Gaussian models with the continuous response variables stunting (height-for-age), underweight (weight-for-age), and wasting (weight-for-height) as indicators of nutritional status in our case study. In a second step, based on the results of the first step, we apply the geoadditive Gaussian latent variable model for continuous indicators in which the 3 measurements of the malnutrition status of children are assumed as indicators for the latent variable “nutritional status”

Analysis of Childhood Diseases and Malnutrition in Developing Countries of Africa

The objective of this work is to examine the impact of socioeconomic and public health factors on childhood diseases and malnutrition in mentioned countries. The causes of child's illness or child's undernutrition are multiple. This work focuses on some risk factors which are assumed to cause the child's diseases and malnutrition as suggested by some previous works (see Kandala, 2001; Adebayo, 2002). Our analysis started with a large number of covariates including a set of bio-demographic and socioeconomic variables, such as current working status of mothers, place of residence, access to toilet facilities, etc (see chapter 2). The analyses are based on data from the 2003 household survey for Egypt and Nigeria for the Demographic and Health Surveys (DHS). More details about the data set are mentioned in the first chapter. The statistical analysis in this thesis is based on modern Bayesian approaches which allow a flexible framework for realistically complex models. These models allow us to analyze usual linear effects of categorical covariates, nonlinear effects of continuous covariates and the geographical effects within a unified semi-parametric Bayesian framework for modelling and inference. A first step of this work is to analyze the effects of the different types of covariates on response variables, diarrhea, fever, and cough which represent the child's diseases in our application. In this step, a Bayesian geoadditive logit model for binary response variables is used (see Fahrmeir and Lang, 2001). In a second step, we employ separate geoadditive probit models (instead of logit models used in the previous step) to the binary listed variables. Based on the results of the separate analyses, we applied geoadditive latent variable probit models (recently suggested by Raach, 2005; Raach and Fahrmeir, 2006) where the three observable disease variables are assumed to be indicators for the latent variable "health status" for the children. In this step, we also compared the results of the separate geoadditive probit models with the results of the latent variable models. As a third step, we used geoadditive Gaussian regression and latent variable models to analyze the malnutrition status of children in both countries. Finally, we used latent variable models for diseases and nutrition indicators together. In the final step, models with one as well as with two latent variables have been estimated using mixed indicators (binary indicators "health status", and continuous indicators "nutrition status") and the results are compared

Analysis of Childhood Diseases and Malnutrition in Developing Countries of Africa

The objective of this work is to examine the impact of socioeconomic and public health factors on childhood diseases and malnutrition in mentioned countries. The causes of child's illness or child's undernutrition are multiple. This work focuses on some risk factors which are assumed to cause the child's diseases and malnutrition as suggested by some previous works (see Kandala, 2001; Adebayo, 2002). Our analysis started with a large number of covariates including a set of bio-demographic and socioeconomic variables, such as current working status of mothers, place of residence, access to toilet facilities, etc (see chapter 2). The analyses are based on data from the 2003 household survey for Egypt and Nigeria for the Demographic and Health Surveys (DHS). More details about the data set are mentioned in the first chapter. The statistical analysis in this thesis is based on modern Bayesian approaches which allow a flexible framework for realistically complex models. These models allow us to analyze usual linear effects of categorical covariates, nonlinear effects of continuous covariates and the geographical effects within a unified semi-parametric Bayesian framework for modelling and inference. A first step of this work is to analyze the effects of the different types of covariates on response variables, diarrhea, fever, and cough which represent the child's diseases in our application. In this step, a Bayesian geoadditive logit model for binary response variables is used (see Fahrmeir and Lang, 2001). In a second step, we employ separate geoadditive probit models (instead of logit models used in the previous step) to the binary listed variables. Based on the results of the separate analyses, we applied geoadditive latent variable probit models (recently suggested by Raach, 2005; Raach and Fahrmeir, 2006) where the three observable disease variables are assumed to be indicators for the latent variable "health status" for the children. In this step, we also compared the results of the separate geoadditive probit models with the results of the latent variable models. As a third step, we used geoadditive Gaussian regression and latent variable models to analyze the malnutrition status of children in both countries. Finally, we used latent variable models for diseases and nutrition indicators together. In the final step, models with one as well as with two latent variables have been estimated using mixed indicators (binary indicators "health status", and continuous indicators "nutrition status") and the results are compared

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

Cardiovascular disease (CVD) is considered to be one of the leading health issues in Thailand. CVD not only contributes to an increase in the number of hospital admissions year by year but also impacts on the rising health care expenditure for the treatment and long-term care of CVD patients. Therefore, this study is aimed at examining the impacts of risk reduction strategies on the number of CVD hospital admissions, Disability-Adjusted Life Years (DALYs) and the costs of hospitalisation. To estimate such impacts a CVD cost-offset model was applied using a Microsoft Excel spreadsheet. The number of the mid-year population was classified by age, gender and the CVD risk factor profiles from the recent Thai National Health Examination Survey (NHES) IV. This survey was chosen as the baseline population. The CVD risk factor profiles included age, gender, systolic blood pressure, total cholesterol, and smoking status. The Asia-Pacific Collaborative Cohort Study (APCCS) equation was applied to predict the probability of developing CVD over the next eight-year period. Estimates on the following were obtained from the model: 1) the CVD events both fatal and non-fatal; 2) the difference between the projected number of deaths and the actual number of deaths in that population; 3) the number of patients who are expected to live with CVD; 4) the DALYs from the estimated number of fatal and non-fatal events; 5) the cost of hospital admissions. Four CVD risk strategy scenarios were investigated as follows: 1) the do-nothing scenario; 2) the optimistic scenario; 3) achieve the UN millennium development goal; and 4) the worst-case scenario. The findings showed that over the next eight years, there are likely to be 3,297,428 recorded cases of CVD; 5,870,049 cases of DALYs; and, approximately ฿57,000 million, ($1.9 billion), is projected as the total cost of hospital admissions. However, if the current health policy can reduce the levels of risk factors as defined in the optimistic scenario or such policy meets the specifications of the UN millennium development goal, there would be a significant reduction in the number of hospital admissions. These are estimated to be a reduction of 522,179 male and 515,416 female cases. With these results, it is expected that health care costs would save approximately ฿9000 million, ($298.3 million), for CVD and 900,000 million DALYs over the next eight years. However, if there is an upward trend in the risk factors as predicted in the worst-case scenario, then there will be an increase of 428,220 CVD cases; consequently, DALYs cases may rise by 766,029 while the hospitalisation costs may increase by approximately ฿7000 million, ($232.1 million). Based on our findings, reducing the levels of CVD risk factors in the population will drastically reduce: 1) the number of CVD cases; 2) DALYs cases; and 3) health care costs. Therefore it is recommended that the health policy should enhance the primary prevention programs which would be targeted at reducing the CVD risk factors in the population

Health care and hospitalisation costs of cardiovascular disease (CVD) in Thailand

Background: Cardiovascular disease (CVD) has become a leading cause of death and disability in Thailand due to the unhealthy lifestyle of the populace; triggering high risk of exposure to CVD, increase in the number of hospital admissions year on year. Objectives: The concerns generated by the inflation in the health care expenditure among service providers motivated this study to examine the costs of hospitalisation of inpatients with (CVD) conditions in Thailand, 2009. Methods: Anonymised secondary data of 327,435 CVD inpatients under “Universal Coverage” (UC) health care scheme were obtained from the National Health Security Office (NHSO), Thailand. The data(51.69%- women and 48.31% - men) were classified using International Classification of Diseases, Tenth Revision (ICD-10) code, of which I20-I25 are Ischemic heart disease (IHD), I60-I69 are stroke and I00- I99areallCVD conditions. Results: Average costs of treatments for all CVD conditions, IHD and stroke were ฿21,921 (£1 = ฿50), ฿32,884 (highest) and ฿25,617.67per patient respectively. Absolute total cost increased with age and the cost of admission of male patients is higher than female. The average (three months) length of stay for stroke patients was found to be the highest. Conclusion: Providers generally spent a total of ฿7,177 million on the treatment of CVD with IHD and stroke taking ฿2,544 million and ฿1,920 million respectivel