Added: Tajuan Langham - Date: 04.03.2022 21:53 - Views: 21967 - Clicks: 6049
Metrics details. In the last two decades, reliable data have been generated in numerous countries through major household surveys, including repeat cross-sectional surveys. What can we learn from these data? These figures are, however, unadjusted for potentially confounding factors, and potentially mask important variation in the practice.
We aim to answer the following questions: 1. In both surveys, unadjusted estimates of the effect of age show no ificant difference in risk of FGM across age cohorts. Other ificant factors are socio-demographic variables, particularly ethnicity. These novel findings fit with predictions of theory on social norms and conventions which suggest that the practice is upheld by interdependent expectations regarding the practice, and when such expectations are challenged within a community, the possibility for abandonment is opened.
In an effort to track progress, reliable data have been generated in numerous countries through major household surveys, and in some countries repeat surveys have been implemented [ 12 ]. These range from nicking the tissue surrounding the clitoris to the complete removal of the external genitalia.
The most well-known and systematically implemented approach has been the holistic community education program developed by the Senegal-based nongovernmental organization Tostan. This program aims to empower communities to participate in their own social, economic, political and cultural development [ 5 ]. There were a total of individual interviews conducted for women from among the two groups. Bymore than communities in Senegal had participated in public declarations www. It highlights that independent behavior change among individuals is difficult, even if they have become opposed to the practice, because reciprocal expectations and sanctions from violating social norms make it costly for an individual or family to opt out [ 11 ].
Communicating changed social norms and expectations, and coordinating behavior change among interconnected social actors allows a critical mass of individuals to move to a new equilibrium, and alter behavior without experiencing negative repercussions. First, the DHS employs a multistage sampling strategy that involves cluster sampling to draw upon women responders; this creates an analytical challenge because observational units are not independent.
Hence, statistical analyses that rely on the assumption of independence, such as standard probit and logistic models, are no longer valid. Second, knowledge of nonlinear effects for some covariates means that it is not possible to assume strictly linear predictors. Analytical approaches have been developed to handle each of these issues. While this approach provides unbiased estimates when individual-level observations are not independent, it does not address the structured spatial effects that arise from cluster sampling.
The DHS clusters often include more than one village that are close to each other and share common risk factors, and consequently, the assumption of independence at the geographical level, such as province or region, is not correct. The independence assumption has an inherent problem of consistency: if the location of the event matters, it makes sense to assume that areas close to each other are more similar than those far apart. This novel approach involves using geo-additive, semi-parametric models that control for spatial dependence and possible nonlinear and time-varying covariates.
Specifically, we investigate the following questions:. The Republic of Senegal, with a population of more than The country is divided into 11 administrative regions Fig. The core questionnaire for households collects data from adult women age 15—49 and men from a nationally representative probability sample of households.
We are currently undertaking the analyses of daughter data, which is forthcoming and will be presented elsewhere. The sampling strategy for each survey was deed to be nationally representative, and provide information for each region. A two stage sampling process was employed.
In the first stage clusters inand in —11 were selected from a list of enumeration areas with probability proportional to size. In the second stage, a complete household listing was completed in each selected cluster, followed by the random selection of 21 households per cluster. In each household, all women age 15—49 were interviewed. In survey data were collected from a total of households and 14, women aged 15—49, with a response rate of The —11 survey selected households, and collected data from 15, women aged 15—49, with a response rate of Further details on sampling can be found elsewhere [ 18 ].
These factors are explored within a simultaneous, coherent regression framework, using a geo-additive, semi-parametric mixed model that simultaneously controls spatial dependence and possibly nonlinear or time effects of covariates and the complex sampling de [ 15 ].
However, geographical associations with prevalence have been neglected. This modelling draws on Bayesian geo-additive methods of spatial statistics, taking advantage of advances in Geographic Information Systems [ 1519 ]. The modelling of the two components is done tly in one estimation procedure that thereby simultaneously identifies socioeconomic determinants, and the spatial effects that are not explained by these socioeconomic determinants while ing for the complex sampling scheme.
Nested data in survey studies is often the rule rather than the exception. Health and survival information of women and their children are nested within family, the clustering of families living within regions. In fact, heterogeneity is often present and frequently the available predictor variables do not explain this heterogeneity sufficiently, see [ 2021 ]. With recent computational advances in statistics it is becoming increasingly straightforward to describe such heterogeneity with mixture models that employ unobserved predictors in a Bayesian hierarchical structure.
Administratively, Senegal was divided in 11 administrative regions in and regions were subdivided into 34 departments, which in turn included 66 urban communes, 94 arrondissements and rural communities. The sample is drawn through stratified clustered sampling and draws, in the case of the SDHS, clusters urban areas and in rural areas from 34 departments from 11 regions. In total, households were drawn at random to select a representative national sample of 12, women aged 15—49 and men aged 15— The response rates were However, one cannot assume that the clusters selected in each region are fully representative of the region in which they are located, as the surveys only attempted to generate a fully representative sample at the national level.
Consequently, the spatial analysis will be affected by some random fluctuations. Some of this random variation can be reduced through the relaxation of the independence assumption between neighbouring states. Such a spatial analysis should preferably be applied to census data, where there is higher clustering at the highly disaggregated sub-national level and the precision of the spatial analysis would be much higher. We assess the likely impact of the neglect of hierarchical structure and geographical location in analyses of the SDHS data that ignore correlation structure and dependence in the data.
The neglect of the geographical location where the respondent lives le to underestimation of standard errors of the fixed effects that inflates the apparent ificance of the estimates [ 1522 ]. Our analysis includes this correlation structure and s for the dependence of neighbouring communities regions in the model.
This gives more reliable estimates of the fixed effect standard error. More detailed information about the formulated models is presented in the appendix.
In this paper we present from multivariate analyses that highlight patterns in the data after adjusting for the effect of proximate variables. The two survey populations are similar in terms of the mean ages of women for the mean age was Most of the population sampled lived in rural settings A total of Such regions were Kaolack and Kolda. The effect disappeared also for small and middle family size; the statistically ificant effect remains only for women living in rural areas, with no education, partners with primary education, in all wealth quintiles, all ethnicities, and the region of residence.
Women with no education were 3. A statistically ificant effect remained only for the wealth index, ethnicity and religion. Women from the poorest quintile were 5. In terms of policy, this implies that children of women from the Poular and Mandingu ethnic groups are at higher risk of undergoing FGM compared to their counterparts in the Wolof ethnic group.
By the time of the —11 survey, 3 regions had been subdivided; to appropriately compare trends in prevalence, we reported the unweighted average of subdivided regions in — It reduced slightly in the low prevalence regions of Diourbel 2. Region of residence was modelled as a spatial variable in Figs.
The modelled covariate confirmed what was observed in the logistic regression analysis but the patterns differ markedly with region of residence and age remaining a ificant risk factors in both surveys. Overall, of Fig. Shown are the posterior odds ratios. Left: Adjusted total residual spatial effects for women circumcision, at regions level in Senegal in The shown in Figs. Specifically, the left-hand map in Fig. However, the total spatial residuals in Figs. At age 40, this probability decreases quickly as age increases, although the variation in probability increases rapidly at the same time.
It is worth mentioning that at first glance, the figures seem to be different.Senegal sex personals adult classifieds
email: [email protected] - phone:(206) 155-5944 x 6269
Trends in female genital mutilation/cutting in Senegal: what can we learn from successive household surveys in sub-Saharan African countries?