Trends and factors related to adolescent pregnancies: an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data
Ontology highlight
ABSTRACT: Background
We aimed to identify the 2001–2013 incidence trend, and characteristics associated with adolescent pregnancies reported by 20–24-year-old women. Methods
A retrospective analysis of the Cuatro Santos Northern Nicaragua Health and Demographic Surveillance 2004–2014 data on women aged 15–19 and 20–24. To calculate adolescent birth and pregnancy rates, we used the first live birth at ages 10–14 and 15–19 years reported by women aged 15–19 and 20–24 years, respectively, along with estimates of annual incidence rates reported by women aged 20–24 years. We conducted conditional inference tree analyses using 52 variables to identify characteristics associated with adolescent pregnancies. Results
The number of first live births reported by women aged 20–24 years was 361 during the study period. Adolescent pregnancies and live births decreased from 2004 to 2009 and thereafter increased up to 2014. The adolescent pregnancy incidence (persons-years) trend dropped from 2001 (75.1 per 1000) to 2007 (27.2 per 1000), followed by a steep upward trend from 2007 to 2008 (19.1 per 1000) that increased in 2013 (26.5 per 1000). Associated factors with adolescent pregnancy were living in low-education households, where most adults in the household were working, and high proportion of adolescent pregnancies in the local community. Wealth was not linked to teenage pregnancies. Conclusions
Interventions to prevent adolescent pregnancy are imperative and must bear into account the context that influences the culture of early motherhood and lead to socioeconomic and health gains in resource-poor settings. Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-021-04215-4.
SUBMITTER: Perez W
PROVIDER: S-EPMC8569964 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The demographic history of a population drives the pattern of genetic variation and is encoded in the gene-genealogical trees of the sampled alleles. However, existing methods to infer demographic history from genetic data tend to use relatively low-dimensional summaries of the genealogy, such as allele frequency spectra. As a step toward capturing more of the information encoded in the genome-wide sequence of genealogical trees, here we propose a novel framework called the genealogical likelihood (gLike), which derives the full likelihood of a genealogical tree under any hypothesized demographic history. Employing a graph-based structure, gLike summarizes across independent trees the relationships among all lineages in a tree with all possible trajectories of population memberships through time and efficiently computes the exact marginal probability under a parameterized demographic model. Through extensive simulations and empirical applications on populations that have experienced multiple admixtures, we showed that gLike can accurately estimate dozens of demographic parameters when the true genealogy is known, including ancestral population sizes, admixture timing, and admixture proportions. Moreover, when using genealogical trees inferred from genetic data, we showed that gLike outperformed conventional demographic inference methods that leverage only the allele-frequency spectrum and yielded parameter estimates that align with established historical knowledge of the past demographic histories for populations like Latino Americans and Native Hawaiians. Furthermore, our framework can trace ancestral histories by analyzing a sample from the admixed population without proxies for its source populations, removing the need to sample ancestral populations that may no longer exist. Taken together, our proposed gLike framework harnesses underutilized genealogical information to offer exceptional sensitivity and accuracy in inferring complex demographies for humans and other species, particularly as estimation of genome-wide genealogies improves.
| S-EPMC10592779 | biostudies-literature
Project description:BackgroundIn order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases.MethodsUsing variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome "poverty" defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in order to assess and rank the importance of predictors about their ability to explain the variation of the outcome "poverty." The majority of the Cuatro Santos households provided information and the included variables measured housing conditions, assets, and demographic experiences since the last update (5?yrs), earlier participation in interventions and food security during the last 4 weeks.ResultsPoverty was rare in households that have some assets and someone in the household that has a higher education than primary school. For these households participating in the intervention that installed piped water with water meter was most important, but also when excluding this variable, the resulting tree showed the same results. When assets were not taken into consideration, the importance of education was pronounced as a predictor for welfare. The results were further strengthened by the validation using Conditional random forest modeling showing the same variables being important as predicting the outcome in the CI tree analysis. As assets can be a result, rather than a predictor of more affluence our results in summary point specifically to the importance of education and participation in the water installation intervention as predictors for more affluence.ConclusionPredictors of poverty are useful for directing interventions and in the Cuatro Santos area education seems most important to prioritize. Hopefully, the lessons learned can continue to develop the Cuatro Santos communities as well as development in similar poor rural settings around the world.
| S-EPMC6819397 | biostudies-literature
Project description:BACKGROUND: The relationship between entomological measures of malaria transmission intensity and mortality remains uncertain. This is partly because transmission is heterogeneous even within small geographical areas. Studying this relationship requires high resolution, spatially structured, longitudinal entomological data. Geostatistical models that have been used to analyse the spatio-temporal heterogeneity have not considered the uncertainty in both sporozoite rate (SR) and mosquito density data. This study analysed data from Kassena-Nankana districts in northern Ghana to obtain small area estimates of malaria transmission rates allowing for this uncertainty. METHODS: Independent Bayesian geostatistical models for sporozoite rate and mosquito density were fitted to produce explicit entomological inoculation rate (EIR) estimates for small areas and short time periods, controlling for environmental factors. RESULTS: Mosquitoes were trapped from 2,803 unique locations for three years using mainly CDC light traps. Anopheles gambiae constituted 52%, the rest were Anopheles funestus. Mean biting rates for An. funestus and An. gambiae were 32 and 33 respectively. Most bites occurred in September, the wettest month. The sporozoite rates were higher in the dry periods of the last two years compared with the wet period. The annual EIR varied from 1,132 to 157 infective bites. Monthly EIR varied between zero and 388 infective bites. Spatial correlation for SR was lower than that of mosquito densities. CONCLUSION: This study confirms the presence of spatio-temporal heterogeneity in malaria transmission within a small geographical area. Spatial variance was stronger than temporal especially in the SR. The estimated EIR will be used in mortality analysis for the area.
| S-EPMC3618087 | biostudies-other
Project description:BackgroundCoalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory only consider very simple demographic scenarios of population size changes, such as exponential growth.ResultsHere we develop a coalescent approach that allows Bayesian non-parametric estimation of the demographic history using genealogies reconstructed from sampled DNA sequences. In this framework inference and model selection is done using reversible jump Markov chain Monte Carlo (MCMC). This method is computationally efficient and overcomes the limitations of related non-parametric approaches such as the skyline plot. We validate the approach using simulated data. Subsequently, we reanalyze HIV-1 sequence data from Central Africa and Hepatitis C virus (HCV) data from Egypt.ConclusionsThe new method provides a Bayesian procedure for non-parametric estimation of the demographic history. By construction it additionally provides confidence limits and may be used jointly with other MCMC-based coalescent approaches.
| S-EPMC548300 | biostudies-literature
Project description:BackgroundMaternal education is universally recognised as a major factor in positive societal indicators (health, wellbeing, overall education, etc.) and a country's growth and economic sustainability, yet the underlying factors contributing to maternal education have not been widely investigated, especially in developing countries.ObjectiveThis study investigated the 15-year trend of maternal education in Bangladesh (2004-2018) to identify the factors contributing to maternal education.MethodThis study used publicly available cross-data from five consecutive Bangladesh Demographic and Health Surveys (2004, 2007, 2011, 2014 and 2018). Level of maternal education was grouped as 'no education', 'incomplete primary', 'complete primary', 'incomplete secondary', 'complete secondary' and 'higher education' (reference group). The differences in factors/exposure variables suspected to contribute to maternal education were measured for these groups for 2004-2018, and a survey multinomial logistic regression was performed to estimate the explanatory value of these factors.ResultsFrom 2004-2018, there was a 62% gross reduction of the no education group and a 61% gross increase in the higher education group. A gross increase was also observed for complete secondary (49%), incomplete secondary (39%) and complete primary education (14%). In multivariate analysis, in rural areas, in 2018, the probability of a woman being in the complete primary, incomplete primary or no education groups was increased (adjusted relative risk ratio: 1.21, 1.40 and 1.59), compared to 2004 (0.73, 1.09, 1.12), respectively. From 2004-2018, the factor of no television watching reduced the probability of maternal education levels. Having a husband/partner who had no education increased the probability of a woman's education level. The probability of all maternal education levels decreased across all wealth index groups.ConclusionThe data suggest that average maternal education level in Bangladesh increased from 2004-2018. However, an integrated effort is required to improve factors associated with maternal education to both increase maternal education and Bangladesh's long-term sustainability.
| S-EPMC8803158 | biostudies-literature
Project description:COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. This study has two objectives. First, we quantify the associations between common socio-demographic variables (including, but not limited to, age, ethnicity, and income) and vaccine acceptance in the U.S. Second, we use a conditional inference tree to quantify and visualize the interaction and conditional effects of relevant socio-demographic variables, known to be important correlates of vaccine acceptance in the U.S., on vaccine acceptance. We conduct a retrospective analysis on a COVID-19 cross-sectional Gallup survey data administered to a representative sample of U.S.-based respondents. Our univariate regression results indicate that most socio-demographic variables, such as age, education, level of household income and education, have significant association with vaccine acceptance, although there are key points of disagreement with the global survey. Similarly, our conditional inference tree model shows that trust in the (former) Trump administration, age and ethnicity are the most important covariates for predicting vaccine hesitancy. Our model also highlights the interdependencies between these variables using a tree-like visualization.
| S-EPMC10180637 | biostudies-literature
Project description:We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015. Interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, microcredit, technical training, university education stipends, and use of the Internet. In 2004, a survey of basic health and demographic information was performed in the whole population, followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals. Information included the house material (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, food security, household assets, and women's self-rated health were collected in 2014. A K-means algorithm was used to cluster the household data (56 variables) in six clusters. The poverty ranking of household clusters using the unsatisfied basic needs index variables changed when including variables describing basic capabilities. The households in the fairly rich cluster with assets such as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity, were labeled vulnerable. Poor and poorest clusters of households were traditional, e.g., in using horses for transport. Results displayed a society transforming from traditional to modern, where the forerunners were not the richest but educated, had more working members in household, had fewer children, and were food secure. Those lagging were the poor, traditional, and food insecure. The approach may be useful for an improved understanding of poverty and to direct local policy and interventions.
| S-EPMC7000462 | biostudies-literature
Project description:A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating groupwise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.
| S-EPMC6319031 | biostudies-other
Project description:The Magu Health and Demographic Surveillance System (Magu HDSS) is part of Kisesa OpenCohort HIV Study located in a rural area of North-Western Tanzania. Since its establishment in 1994, information on pregnancies, births, marriages, migrations and deaths have been monitored and updated between one and three times a year by trained fieldworkers. Other research activities implemented in the cohort include: sero surveys which have been conducted every 2-3 years to collect socioeconomic data, HIV sero status and health knowledge attitude and behaviour in adults aged 15 years or more living in the area; verbal autopsy (VA) interviews conducted to establish cause of death in all deaths encountered in the area; Llnking data collected at health facilities to community-based data; monitoring voluntary counselling and testing (VCT); and assessing uptake of antiretroviral treatment (ART). In addition, within the community, qualitative studies have been conducted to address issues linked to HIV stigma, the perception of ART access and adherence.In 2014, the population was over 35?000 individuals. Magu HDSS has contributed to Tanzanian estimates of fertility and mortality, and is a member of the INDEPTH network. Demographic data for Magu HDSS are available via the INDEPTH Network's Sharing and Accessing Repository (iSHARE) and applications to access HDSS data for collaborative analysis are encouraged.
| S-EPMC4911678 | biostudies-literature
Project description:BackgroundThe fundamental approach to improve maternal and neonatal health is increasing skilled delivery rate. Women giving birth at health institutions can prevent maternal and neonatal deaths by getting skilled birth attendance. In Ethiopia, despite a significant decrease in maternal mortality over the past decade, still a significant number of women give birth at home. Moreover, evidence from population-based longitudinal studies on skilled delivery is limited. Therefore, this study aims to investigate the magnitude, trend, and determinants of skilled delivery in Kilite-Awlaelo Health Demographic Surveillance System (KA-HDSS), Northern Ethiopia.MethodPopulation-based longitudinal study design was conducted by extracting data for nine consecutive years (2009-2017) from KA-HDSS database. In order to measure the trends of skilled delivery, KA-HDSS data sets were analyzed (2009-2017). Bivariate and multivariate analyses were performed using STATA version 16. A multivariable binary logistic regression model was fitted to assess determinants of skilled delivery and odds ratio with 95% CI was used to assess presence of associations at a 0.05 level of significance.ResultsThe skilled delivery rate have continuously increased among reproductive age women from 15.12% (95% CI: 13.30% - 17.09%) in 2010 to 95.85% (95% CI: 94.58% - 96.895%) in 2017. The skilled delivery rate becomes high (> = 82) in the period of 2014-2017. Education, residence, marital status, occupation and antenatal care (ANC) visits were the most important determinants for skilled delivery among reproductive age women during the period of high skilled delivery rate (2014-2017). Women urban dwellers had about 28 times (AOR = 27.66; 95% CI: 3.86-196.97) higher odds to deliver by skilled birth attendants than rural dwellers. Unmarried women who gave birth were 2.18 (AOR: 2.18; 95% CI: 1.30-3.64) times more likely to have skilled delivery service compared to those married. Likewise, women with four or more ANC visits were 3.2 times more likely to undergo skilled delivery service than those having no ANC visits (AOR: 3.16; 95% CI: 2.33-4.28). Moreover, women having at least a secondary education were 2 times more likely to have skilled delivery service compared to those women with no formal education (AOR = 2.10, 95% CI: 1.18-3.74).ConclusionRegardless of the importance of health facility delivery, a significant number of women still deliver at home attended by unskilled birth attendants. There has been a substantial increase in use of health facilities for delivery among women in the reproductive age. The factors affecting skilled delivery among reproductive age women were educational level, residence, marital status, occupation and use of ANC service. Maternal health related interventions are needed to change women's attitudes towards skilled delivery. Moreover, ANC coverage should be increased to improve skilled delivery service.
| S-EPMC8428565 | biostudies-literature