| 1. |
- Bucardo, Filemón, et al.
(författare)
-
Association of Genetic Polymorphisms in DC-SIGN, Toll-Like Receptor 3, and Tumor Necrosis Factor a Genes and the Lewis-Negative Phenotype With Chikungunya Infection and Disease in Nicaragua
- 2021
-
Ingår i: Journal of Infectious Diseases. - : Oxford University Press. - 0022-1899 .- 1537-6613. ; 223:2, s. 278-286
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundChikungunya infections range from subclinical infection to debilitating arthralgia and to chronic inflammatory rheumatism. Tumor necrosis factor (TNF) α, DC-SIGN (dendritic cell–specific intercellular adhesion molecule 3–grabbing nonintegrin), Toll-like receptor (TLR) 3, and blood groups have been directly or indirectly implicated in the susceptibility and pathogenesis of chikungunya.MethodsTo test the hypothesis that polymorphisms in genes coding for these molecules determine clinical outcomes of chikungunya infection, a retrospective case-control study was performed in León, Nicaragua. The study included 132 case patients and 132 controls, matched for age, sex and neighborhood. Case patients had clinical symptoms of chikungunya, which was diagnosed by means of polymerase chain reaction. Controls were individuals not reporting abrupt presentation of clinical chikungunya-like symptoms. Polymorphisms were identified by TaqMan single-nucleotide polymorphism genotyping assays.ResultsAfter adjustment for sociodemographic risk factors, chikungunya disease was associated with polymorphism in DC-SIGN and TLR3 genes (odds ratios, 5.2 and 3.3, respectively), and TNF-α with reduced persistent joint pain (0.24). Persistent joint pain was also associated with age, female sex and other comorbid conditions. Most interestingly, the Lewis-negative phenotype was strongly associated with both symptomatic chikungunya and immunoglobulin G seropositivity (odds ratios, 2.7, and 3.3, respectively).ConclusionThis study identified polymorphisms in DC-SIGN, TLR3, and TNF-α genes as well as Lewis-negative phenotype as risk factors for chikungunya infection and disease progression.
|
|
| 2. |
- Pérez, Wilton, 1979-, et al.
(författare)
-
Trends and factors related to adolescent pregnancies : an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data
- 2021
-
Ingår i: BMC Pregnancy and Childbirth. - : BioMed Central. - 1471-2393 .- 1471-2393. ; 21:1
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 3. |
- Källestål, Carina, 1954-, et al.
(författare)
-
Predicting poverty : data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua
- 2019
-
Ingår i: International Journal for Equity in Health. - : BioMed Central. - 1475-9276. ; 18:1
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: In 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.METHODS: Using 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.RESULTS: Poverty 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.CONCLUSION: Predictors 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.
|
|
| 4. |
- Källestål, Carina, 1954-, et al.
(författare)
-
Assessing the Multiple Dimensions of Poverty : Data Mining Approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua
- 2020
-
Ingår i: Frontiers in Public Health. - : FRONTIERS MEDIA SA. - 2296-2565. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
