Behaviour Risk Factor Surveillance Data Analysis Using Varying Coefficient Models

There is a high potential of information available in Behaviour Risk Factor Surveillance (BRFS) data, and especially for studying trends, as these data collect information in an ongoing and almost continuous manner for long periods of time. In order to account for the complex and dynamic relationships between the variables and avoid the aggregation of measures so as not to lose information in variability, the use of varying coefficient models with non-parametric techniques have been studied. These models allow the study of the trends and inter-relationships in the effects of the variables on the outcome of interest either over time or space, therefore providing valuable information for health policy interventions. A comparison of the possible estimation techniques, using the Italian surveillance data, has resulted in the selection of P-splines for estimation due to the flexibility in their use and the faster computation times. This estimation method was applied for a time varying coefficient model for a smoking status outcome variable using Italian surveillance data, and a time varying coefficient model for an obesity status outcome variable using U.S.A. surveillance data. The results of these models provide coefficient plots in which one can observe which subgroups of the population have an effect on the outcome which is changing over time. A spatial varying coefficient model was also studied for one point in time using smoothing spline estimation with tensor product smooths, and the maps produced from this model were able to show how the probabilities of the outcome variable (obesity) are changing across the counties of a U.S. state within each population subgroup. The strengths and limitations of these methods are discussed, as well as recommendations for further research such as the study of a spatial-temporal model using health surveillance data. Notwithstanding few limitations, the varying coefficient model represents an effective approach proving to produce interesting results (not accessible with the usual standard epidemiological approach) in this particular field of application and with BRFS data

Using geographical analysis to identify child health inequality in sub-Saharan Africa

One challenge to achieving Millennium Development Goals was inequitable access to quality health services. In order to achieve the Sustainable Development Goals, interventions need to reach underserved populations. Analyzing health indicators in small geographic units aids the identification of hotspots where coverage lags behind neighboring areas. The purpose of these analyses is to identify areas of low coverage or high need in order to inform effective resource allocation to reduce child health inequity between and within countries. Using data from The Demographic and Health Survey Program surveys conducted in 27 selected African countries between 2010 and 2014, we computed estimates for six child health indicators for subnational regions. We calculated Global Moran’s I statistics and used Local Indicator of Spatial Association analysis to produce a spatial layer showing spatial associations. We created maps to visualize sub-national autocorrelation and spatial clusters. The Global Moran’s I statistic was positive for each indicator (range: 0.41 to 0.68), and statistically significant (p <0.05), suggesting spatial autocorrelation across national borders, and highlighting the need to examine health indicators both across countries and within them. Patterns of substantial differences among contiguous subareas were apparent; the average intra-country difference for each indicator exceeded 20 percentage points. Clusters of cross-border associations were also apparent, facilitating the identification of hotspots and informing the allocation of resources to reduce child health inequity between and within countries. This study exposes differences in health indicators in contiguous geographic areas, indicating that specific regional and subnational, in addition to national, strategies to improve health and reduce health inequalities are warranted

Evaluating quality of contraceptive counseling: An analysis of the method information index

The Method Information Index (MII) is calculated from contraceptive users\u27 responses to questions regarding counseling content-whether they were informed about methods other than the one they received, told about method-specific side effects, and advised what to do if they experienced side effects. The MII is increasingly reported in national surveys and used to track program performance, but little is known about its properties. Using additional questions, we assessed the consistency between responses and the method received in a prospective, multicountry study. We employed two definitions of consistency: (1) presence of any concordant response, and (2) absence of discordant responses. Consistency was high when asking whether users were informed about other methods and what to do about side effects. Responses were least consistent when asking whether side effects were mentioned. Adjusting for inconsistency, scores were up to 50 percent and 30 percent lower in Pakistan and Uganda, respectively, compared to unadjusted MII scores. Additional questions facilitated better understanding of counseling quality

Behaviour Risk Factor Surveillance Data Analysis Using Varying Coefficient Models

There is a high potential of information available in Behaviour Risk Factor Surveillance (BRFS) data, and especially for studying trends, as these data collect information in an ongoing and almost continuous manner for long periods of time. In order to account for the complex and dynamic relationships between the variables and avoid the aggregation of measures so as not to lose information in variability, the use of varying coefficient models with non-parametric techniques have been studied. These models allow the study of the trends and inter-relationships in the effects of the variables on the outcome of interest either over time or space, therefore providing valuable information for health policy interventions. A comparison of the possible estimation techniques, using the Italian surveillance data, has resulted in the selection of P-splines for estimation due to the flexibility in their use and the faster computation times. This estimation method was applied for a time varying coefficient model for a smoking status outcome variable using Italian surveillance data, and a time varying coefficient model for an obesity status outcome variable using U.S.A. surveillance data. The results of these models provide coefficient plots in which one can observe which subgroups of the population have an effect on the outcome which is changing over time. A spatial varying coefficient model was also studied for one point in time using smoothing spline estimation with tensor product smooths, and the maps produced from this model were able to show how the probabilities of the outcome variable (obesity) are changing across the counties of a U.S. state within each population subgroup. The strengths and limitations of these methods are discussed, as well as recommendations for further research such as the study of a spatial-temporal model using health surveillance data. Notwithstanding few limitations, the varying coefficient model represents an effective approach proving to produce interesting results (not accessible with the usual standard epidemiological approach) in this particular field of application and with BRFS data.C'è un alto potenziale di informazioni disponibili nei dati di sorveglianza sui fattori comportamentali di rischio, specialmente per lo studio di tendenze evolutive nella popolazione: questi dati vengono infatti raccolti in modo quasi continuo e per lunghi periodi temporali. Per spiegare le relazioni complesse e le dinamiche tra le variabili, evitando l'aggregazione di misure per non perdere l'informazione sulla variabilità, è stata studiata la possibilità di applicare a questi dati modelli a coefficienti variabili con tecniche non parametriche. Questi modelli permettono lo studio delle tendenze e delle interrelazioni negli effetti delle variabili sul risultato di interesse nel tempo o nello spazio, fornendo quindi informazioni preziose per gli interventi di politica sanitaria. Un confronto delle possibili tecniche di stima, utilizzando i dati di sorveglianza italiani, ha portato alla selezione delle P-spline perché più flessibili nel loro utilizzo e computazionalmente più veloci. Questo metodo di stima è stato applicato ad un modello a coefficienti variabili nel tempo per lo studio di una variabile risposta sulle abitudini al fumo utilizzando i dati di sorveglianza italiani. Inoltre, è stato studiato un modello a coefficienti variabili nel tempo per l'esito di una variabile risposta sullo stato di obesità utilizzando i dati di sorveglianza statunitensi. Dai risultati derivanti dall’applicazione di questi modelli vengono prodotti grafici (di coefficienti e OR) utili per osservare quali sottogruppi della popolazione presentano effetti che stanno evolvendo nel tempo. Anche un modello a coefficienti spazialmente variabili è stato studiato (in riferimento ad un determinato momento temporale) utilizzando stime spline con lisciature fornite dal prodotto tensoriale. Le mappe prodotte da questo modello sono state in grado di evidenziare come le probabilità della variabile risposta (obesità) stanno cambiando attraverso le contee di un stato negli USA all'interno di ogni sottogruppo della popolazione. I punti di forza e i limiti di questi metodi sono stati discussi, inoltre alcune raccomandazioni per ulteriori ricerche sono state proposte per lo studio di un modello spazio-temporale utilizzando i dati di sorveglianza sanitaria. Nonostante alcune limitazioni, il modello a coefficienti variabili rappresenta un approccio efficace dimostrando di produrre risultati interessanti (non accessibili con il consueto e tipico approccio epidemiologico) in questo particolare campo applicativo

Stunting and Anemia in Children from Urban Poor Environments in 28 Low and Middle-income Countries: A Meta-analysis of Demographic and Health Survey Data

Child malnutrition remains a global concern with implications not only for children&rsquo;s health and cognitive function, but also for countries&rsquo; economic growth. Recent reports suggest that global nutrition targets will not be met by 2025. Large gaps are evident between and within countries. One of the largest disparities in child malnutrition within counties is between urban and rural children. Large disparities also exist in urban areas that have higher rates of child malnutrition in the urban poor areas or slums. This paper examines stunting and anemia related to an urban poverty measure in children under age 5 in 28 low and middle-income countries with Demographic and Health Survey data. We used the United Nations Human Settlements Programme (UN-HABITAT) definition to define urban poor areas as a proxy for slums. The results show that in several countries, children had a higher risk of stunting and anemia in urban poor areas compared to children in urban non-poor areas. In some countries, this risk was similar to the risk between the rural and urban non-poor. Tests of heterogeneity showed that these results were not homogeneous across countries. These results help to identify areas of greater disadvantage and the required interventions for stunting and anemia

Quality of care in family planning services in Senegal and their outcomes

Abstract Background High quality of care in family planning (FP) services has been found to be associated with increased and continued use of contraceptive methods. The interpersonal skills and technical competence of the provider is one of the main components of quality of care. To study the process component of quality of care, the distribution of the FP counseling topics was examined by client, provider and facility characteristics. To assess the outcomes of quality of care, client satisfaction and their knowledge of their method’s protection from STIs were used. This study examined the factors associated with these outcomes with a focus on provider counseling and training. Methods Data from the 2012–2013 Senegal Service Provision Assessment survey was used for the analysis. The survey included a representative sample of the health facilities in Senegal and collects data by observing the clients’ FP visits and conducting exit interviews. The main outcomes of interest were provider’s counseling in FP, client’s satisfaction with FP services and client’s knowledge of their method’s protection from STIs. Several covariates were used in the analysis which represent client, provider and facility characteristics. Results The level of counseling was inadequate-- very low proportions of providers that performed different types of counseling. Counseling was more likely to be provided to new than returning clients. Approximately 84% of the clients were very satisfied with services but only 58% had correct knowledge of their method’s protection from STIs. Clients were significantly less likely to be very satisfied when their providers counseled on side effects and when to return, and counseling provided on method’s protection from STIs did not significantly improve knowledge in this area. Clients seen by a provider with FP training had almost twice the odds of having correct knowledge about their method’s protection from STIs compared with clients seen by a provider with no recent training. Conclusions The percentage of providers offering FP counseling to their clients was relatively low and was ineffective on the client-focused outcomes. Interventions may be required for more effective counseling methods that are client-centered as well as providing more FP training to providers