Investigating improvements in premature death in two rural, majority-minority counties in the south.
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ABSTRACT:
SUBMITTER: Lyn R
PROVIDER: S-EPMC7334465 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
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Project description:Poor sanitation worldwide leads to an annual loss of approximately $222.9 billion and is the second leading cause of Disability-Adjusted Life Years (DALY's) lost due to diarrhoea. Yet in Kenya, the slow rate and levels at which the household's access improved sanitation facilities remain a concern, and it is unknown if the cost of new technologies is a barrier to access. This study assessed the maximum willingness to pay (WTP) for SAFI and SATO sanitation products and identified those factors that affect the willingness to pay (WTP) valuation estimates by households in three counties in Kenya. It used quantitative economic evaluation research integrated within a cross-sectional survey. Contingent valuation method (CVM) was used to determine the maximum WTP for sanitation in households. We used the logistic regression model in data analysis. A total of 211 households were interviewed in each county, giving a total sample size of 633 households. The mean WTP for SAFI latrines was $153.39 per household, while the mean WTP for SATO pans and SATO stools was $11.49 and $14.77 respectively. For SAFI latrines, households in Kakamega were willing to pay $6.6 more than average while in Siaya, the households were willing to pay $5.1 less than the average. The main determinants of households WTP for the two sanitation products included household's proximity to the toilet (p = 0.0001), household income (β = .2245741, p = 0.004), sanitation product (β = -2968.091; p = 0.004), socioeconomic status (β = -3305.728, p = 0.004) and a household's satisfaction level with the current toilet (β = -4570.602; p = 0.0001). Increased proximity of households to the toilet, higher incomes, and providing loan facilities or subsidy to poor households could increase the demand for these sanitation technologies.
| S-EPMC8092787 | biostudies-literature
Project description:Background Disparities in premature cardiac death (PCD) might stagnate the progress toward the reduction of PCD in the United States and worldwide. We estimated disparities across US counties in PCD rates and investigated county-level factors related to the disparities. Methods and Results We used US mortality data for cause-of-death and demographic data from death certificates and county-level characteristics data from multiple databases. PCD was defined as any death that occurred at an age between 35 and 74 years with an underlying cause of death caused by cardiac disease based on International Classification of Diseases, Tenth Revision (ICD-10), codes. Of the 1 598 173 PCDs that occurred during 1999-2017, 60.9% were out of hospital. Although the PCD rates declined from 1999-2017, the proportion of out-of-hospital PCDs among all cardiac deaths increased from 58.3% to 61.5%. The geographic disparities in PCD rates across counties widened from 1999 (Theil index=0.10) to 2017 (Theil index=0.23), and within-state differences accounted for the majority of disparities (57.4% in 2017). The disparities in out-of-hospital PCD rates (and in-hospital PCD rates) associated with demographic composition were 36.51% (and 37.51%), socioeconomic features were 18.64% (and 18.36%), healthcare environment were 18.64% (and 13.90%), and population health status were 23.73% (and 30.23%). Conclusions Disparities in PCD rates exist across US counties, which may be related to the decelerated trend of decline in the rates among middle-aged adults. The slower declines in out-of-hospital rates warrants more precision targeting and sustained efforts to ensure progress at better levels of health (with lower PCD rates) against PCD.
| S-EPMC7792253 | biostudies-literature
Project description:BackgroundThe risk of premature cardiovascular disease in patients with familial hypercholesterolemia (FH) can be profoundly reduced by cholesterol-lowering therapy, and current guidelines for FH advocate ambitious low-density lipoprotein cholesterol (LDL-C) goals. In the present study, we determined whether these goals are reflected in current clinical practice once FH has been diagnosed.Methodology/principal findingsIn 2008, we sent questionnaires to all subjects (aged 18-65 years) who were molecularly diagnosed with FH in the year 2006 through the screening program in The Netherlands. Of these 1062 subjects, 781 completed the questionnaire (46% males; mean age: 42+/-12 years; mean LDL-C at molecular diagnosis (baseline): 4.1+/-1.3 mmol/L). The number of persons that used cholesterol-lowering therapy increased from 397 (51%) at baseline to 636 (81%) after diagnosis. Mean treated LDL-C levels decreased significantly to 3.2+/-1.1 mmol/L two years after diagnosis. Only 22% achieved the LDL-C target level of < or = 2.5 mmol/L.Conclusions/significanceThe proportion of patients using cholesterol-lowering medication was significantly increased after FH diagnosis through genetic cascade screening. The attained LDL-C levels were lower than those reported in previous surveys on medication use in FH, which could reflect the effect of more stringent lipid target levels. However, only a minority of the medication users reached the LDL-C target.
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Project description:The response of human immunodeficiency virus type 1 (HIV-1) quasispecies to antiretroviral therapy is influenced by the ensemble of mutants that composes the evolving population. Low-abundance subpopulations within HIV-1 quasispecies may determine the viral response to the administered drug combinations. However, routine sequencing assays available to clinical laboratories do not recognize HIV-1 minority variants representing less than 25% of the population. Although several alternative and more sensitive genotyping techniques have been developed, including next-generation sequencing (NGS) methods, they are usually very time consuming, expensive and require highly trained personnel, thus becoming unrealistic approaches in daily clinical practice. Here we describe the development and testing of a HIV-1 genotyping DNA microarray that detects and quantifies, in majority and minority viral subpopulations, relevant mutations and amino acid insertions in 42 codons of the pol gene associated with resistance and multidrug resistance to protease (PR) and reverse transcriptase (RT) inhibitors. A customized bioinformatics protocol has been implemented to analyze the microarray hybridization data by including a new normalization procedure and a stepwise filtering algorithm which resulted in the highly accurate (96.33%) detection of positive/negative signals. This microarray has been tested with 57 subtype B HIV-1 clinical samples extracted from multi-treated patients, showing an overall identification of 95.53% and 89.24% of the queried PR and RT codons, respectively, and enough sensitivity to detect minority subpopulations representing as low as 5-10% of the total quasispecies. Such a genotyping platform represents an efficient diagnostic and prognostic tool useful to personalize antiviral treatments in clinical practice.
2016-11-30 | GSE90621 | GEO
Project description:The mobilities of electrons and holes determine the applicability of any semiconductor, but their individual measurement remains a major challenge. Here, we show that time-resolved terahertz spectroscopy (TRTS) can distinguish the mobilities of minority and majority charge carriers independently of the doping-type and without electrical contacts. To this end, we combine the well-established determination of the sum of electron and hole mobilities from photo-induced THz absorption spectra with mobility-dependent ambipolar modeling of TRTS transients. The method is demonstrated on a polycrystalline Cu2ZnSnSe4 thin film and reveals a minority (electron) mobility of 128?cm2/V-s and a majority (hole) carrier mobility of 7?cm2/V-s in the vertical transport direction relevant for light emitting, photovoltaic and solar water splitting devices. Additionally, the TRTS analysis yields an effective bulk carrier lifetime of 4.4?ns, a surface recombination velocity of 6 * 104?cm/s and a doping concentration of ca. 1016 cm-3, thus offering the potential for contactless screen novel optoelectronic materials.
| S-EPMC6160416 | biostudies-other
Project description:BackgroundThe gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the "eight Americas," to explore the causes of the disparities that can inform specific public health intervention policies and programs.Methods and findingsThe eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15-44 y) and middle-aged (45-59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations.ConclusionsDisparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.
| S-EPMC1564165 | biostudies-literature
Project description:While the rare occurrence of child loss is accompanied by reduced life expectancy of parents in contemporary affluent populations, its impact in developing societies with high child mortality rates is unclear. We identified all parents in Iceland born 1800-1996 and compared the mortality rates of 47,711 parents who lost a child to those of their siblings (N = 126,342) who did not. The proportion of parents who experienced child loss decreased from 61.1% of those born 1800-1880 to 5.2% of those born after 1930. Child loss was consistently associated with increased rate of maternal, but not paternal, death before the age of 50 across all parent birth cohorts; the relative increase in maternal mortality rate ranged from 35% among mothers born 1800-1930 to 64% among mothers born after 1930. The loss of a child poses a threat to the survival of young mothers, even during periods of high infant mortality rates.
| S-EPMC6850766 | biostudies-literature
Project description:The MRC/Wits University Agincourt research centre, part of the INDEPTH Network, has documented mortality in a defined population in the rural northeast of South Africa for 20 years (1992-2011) using long-term health and socio-demographic surveillance. Detail on the unfolding, at times unpredicted, mortality pattern has been published. This experience is reviewed here and updated using more recent data.To present a review and summary of mortality patterns across all age-sex groups in the Agincourt sub-district population for the period 1992-2011 as a comprehensive basis for public health action.Vital events in the Agincourt population have been updated in annual surveys undertaken since 1992. All deaths have been rigorously recorded and followed by verbal autopsy interviews. Responses to questions from these interviews have been processed retrospectively using the WHO 2012 verbal autopsy standard and the InterVA-4 model for assigning causes of death in a standardised manner.Between 1992 and 2011, a total of 12,209 deaths were registered over 1,436,195 person-years of follow-up, giving a crude mortality rate of 8.5 per 1,000 person-years. During the 20-year period, the population experienced a major HIV epidemic, which resulted in more than doubling of overall mortality for an extended period. Recent years show signs of declining mortality, but levels remain above the 1992 baseline recorded using the surveillance system.The Agincourt population has experienced a major mortality shock over the past two decades from which it will take time to recover. The basic epidemic patterns are consistent with generalised mortality patterns observed in South Africa as a whole, but the detailed individual surveillance behind these analyses allows finer-grained analyses of specific causes, age-related risks, and trends over time. These demonstrate the complex, somewhat unpredicted course of mortality transition over the years since the dawn of South Africa's democratic era in 1994.
| S-EPMC4220148 | biostudies-literature
Project description:OBJECTIVE:To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010. DATA SOURCE:Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research. STUDY DESIGN:We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade. PRINCIPAL FINDINGS:Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide. CONCLUSIONS:Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.
| S-EPMC6232410 | biostudies-literature
Project description:PurposeTo determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics.MethodsWe used the Oregon Commission for the Blind registry to determine the number of persons who are legally blind, as well as licensure data to determine the density of eye care providers (optometrists and ophthalmologists) within each county of the State of Oregon. We used geospatial statistics, analysis of variance, and logistic regression to determine the explanatory variables associated with blindness within counties.ResultsWe included 8350 individuals who are legally blind within the state of Oregon in the calendar year 2015. The mean observed prevalence of registered blindness was 0.21% and ranged almost 9-fold from 0.04% to 0.58% among counties (p < .001). In univariate models, higher blindness was associated with increasing median age (p = .027), minority race (p < .001), decreased median household income (p < .001), increased poverty within a county (p < .001), and higher density of ophthalmologists (p = .003). Density of optometrists was not associated with prevalence of blindness (p = .89). The final multivariable model showed higher blindness to be associated with lower median household income, higher proportion of black race, and lower proportion of Hispanic race (p < .001 for all).ConclusionGeospatial analytics identified counties with higher and lower than expected proportions of blindness even when adjusted for sociodemographic factors. Clinicians and researchers may use the methods and results of this study to better understand the distribution of individuals with blindness and the associated factors to help design public health interventions.
| S-EPMC9550029 | biostudies-literature