Dataset Information

Geographic Hotspots for Low Birthweight: An Analysis of Counties With Persistently High Rates.

ABSTRACT: This study evaluated persistency in county-level rates of low birthweight outcomes to identify "hotspot counties" and their associated area-level characteristics. Administrative data from the National Center for Health Statistics Birth Data Files, years 2011 to 2016 were used to calculate annual county-level rates of low birthweight. Counties ranking in the worst quintile (Q5) for ?3?years with a neighboring county in the worst quintile were identified as hotspot counties. Multivariate logistic regression was used to associate county-level characteristics with hotspot designation. Adverse birth outcomes were persistent in poor performing counties, with 52% of counties in Q5 for low birthweight in 2011 remaining in Q5 in 2016. The rate of low birthweight among low birthweight hotspot counties (n?=?495) was 1.6 times the rate of low birthweight among non-hotspot counties (9.3% vs 5.8%). The rate of very low birthweight among very low birthweight hotspot counties (n?=?387) was twice as high compared to non-hotspot counties (1.8% vs 0.9%). A one standard deviation (6.5%) increase in the percentage of adults with at least a high school degree decreased the probability of low birthweight hotspot designation by 1.7?percentage points (P?=?.006). A one standard deviation (20%) increase in the percentage of the population that was of minority race/ethnicity increased hotspot designation for low birthweight by 5.7?percentage points (P?

SUBMITTER: Brown CC

PROVIDER: S-EPMC7550956 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Geographic Hotspots for Low Birthweight: An Analysis of Counties With Persistently High Rates.

Brown Clare C CC Moore Jennifer E JE Felix Holly C HC Stewart Mary K MK Tilford John M JM

Inquiry : a journal of medical care organization, provision and financing 20200101

This study evaluated persistency in county-level rates of low birthweight outcomes to identify "hotspot counties" and their associated area-level characteristics. Administrative data from the National Center for Health Statistics Birth Data Files, years 2011 to 2016 were used to calculate annual county-level rates of low birthweight. Counties ranking in the worst quintile (Q5) for ≥3 years with a neighboring county in the worst quintile were identified as hotspot counties. Multivariate logistic ...[more]

PMID: 33043787

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Project description:We conducted a systematic review and meta-analysis of studies assessing HCV infection rates in haemodialysis patients in Brazil (Prospero CRD #42021275068). We included studies on patients under haemodialysis, comprising both convenience samples and exhaustive information from selected services. Patients underwent HCV serological testing with or without confirmation by HCV RNA PCR. Exclusion criteria were the following: absence of primary empirical information and studies without information on their respective settings, study year, accurate infection rates, or full specification of diagnostic tests. Studies with samples ≤ 30 and serial assessments with repeated information were also excluded. Reference databases included PubMed, LILACS, Scopus, and Web of Science for the period 1989-2019. A systematic review was carried out, followed by two independent meta-analyses: (i) studies with data on HCV prevalence and (ii) studies with a confirmatory PCR (i.e., active infection), respectively. A comprehensive set of different methods and procedures were used: forest plots and respective statistics, polynomial regression, meta-regression, subgroup influence, quality assessment, and trim-and-fill analysis. 29 studies and 11,290 individuals were assessed. The average time patients were in haemodialysis varied from 23.5 to 56.3 months. Prevalence of HCV infection was highly heterogeneous, with a pronounced decrease from 1992 to 2001, followed by a plateau and a slight decrease in recent years. The summary measure for HCV prevalence was 34% (95% CI 26-43%) for studies implemented before 2001. For studies implemented after 2001, the corresponding summary measure was 11% (95% CI 8-15%). Estimates for prevalence of active HCV infection were also highly heterogeneous. There was a marked decline from 1996 to 2001, followed by a plateau and a slight increase after 2010. The summary measure for active HCV infection was 19% (95% CI 15-25%) in studies carried out before 2001. For studies implemented after 2001, the corresponding summary measure was 9% (95% CI 6-13%). Heterogeneity was pervasive, but different analyses helped to identify its underlying sources. Besides the year each study was conducted, the findings differed markedly between geographic regions and were heavily influenced by the size of the studies and publication biases. Our systematic review and meta-analysis documented a substantial decline in HCV prevalence among Brazilian haemodialysis patients from 1992 to 2015. CKD should be targeted with specific interventions to prevent HCV infection, and if prevention fails, prompt diagnosis and treatment. Although the goal of HCV elimination by 2030 in Brazil remains elusive, it is necessary to adopt measures to achieve micro-elimination and to launch initiatives towards targeted interventions to curb the spread of HCV in people with CKD, among other high-risk groups. This is of particular concern in the context of a protracted COVID-19 pandemic and a major economic and political crisis.

Project description:We used a high-density tiling array to estimate genetic recombination rate among 32 independent recombinant progeny of a P. falciparum genetic cross (7G8 M-CM-^W GB4). We detected 3184 segregating multi-probe single-feature polymorphisms (mSFPs) and 638 recombination events (496 excluding those from subtelomeric regions). These data, in combination with results from 254 previously reported microsatellites, enabled us to construct a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb/cM (12.8 kb/cM excluding subtelomeric regions) and identified 54 hotspots, some of which occurred in genes encoding surface antigens or proteins with repetitive motifs that might play a role in genetic recombination in the parasite. Motifs enriched in hotspots were also identified. In agreement with results from a previous cross (HB3 M-BM-4 Dd2), there was positive correlation between sizes of individual chromosomes and their recombination events. These results show that the P. falciparum genome is highly recombinogenic, providing an important genetic basis for parasite survival under various selection pressures. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination frequency observed. Ten microgram of genomic DNA, extracted and purified from 3D7 (reference), thirty-two P. falciparum independent recombinant progeny of the 7G8 x GB4 cross, and the two parental lines (Hayton, 2008), were hybridized to the PFSANGER GenechipM-BM-. (Affymetrix, Inc., Santa Clara, CA, USA). The scanned image CEL files were first processed using the RMA method, then averaged and compared with reference genome 3D7, and lastly assigned either 7G8 or GB4 alleles based on similarities to the two parental lines. Total of 35 genomic DNA samples (biological replicates: 6 for 3D7, 4 for 7G8, 4 for GB4, and 2 for Pf_WE2). The supplementary file 'GSE25656_QuantNormData_Log2_AllSamples.txt' contains the RMA-normalized data for all of the samples. The supplementary files 'GSE25656_chr*' contain the parental allele assignment of each chromosome and include probe-level annotation.

Project description:BackgroundCOVID-19 infection has disproportionately affected socially disadvantaged neighborhoods. Despite this disproportionate burden of infection, these neighborhoods have also lagged in COVID-19 vaccinations. To date, we have little understanding of the ways that various types of social conditions intersect to explain the complex causes of lower COVID-19 vaccination rates in neighborhoods.MethodsWe used configurational comparative methods (CCMs) to study COVID-19 vaccination rates in Philadelphia by neighborhood (proxied by zip code tabulation areas). Specifically, we identified neighborhoods where COVID-19 vaccination rates (per 10,000) were persistently low from March 2021 - May 2021. We then assessed how different combinations of social conditions (pathways) uniquely distinguished neighborhoods with persistently low vaccination rates from the other neighborhoods in the city. Social conditions included measures of economic inequities, racial segregation, education, overcrowding, service employment, public transit use, health insurance and limited English proficiency.ResultsTwo factors consistently distinguished neighborhoods with persistently low COVID-19 vaccination rates from the others: college education and concentrated racial privilege. Two factor values together - low college education AND low/medium concentrated racial privilege - identified persistently low COVID-19 vaccination rates in neighborhoods, with high consistency (0.92) and high coverage (0.86). Different values for education and concentrated racial privilege - medium/high college education OR high concentrated racial privilege - were each sufficient by themselves to explain neighborhoods where COVID-19 vaccination rates were not persistently low, likewise with high consistency (0.93) and high coverage (0.97).ConclusionsPairing CCMs with geospatial mapping can help identify complex relationships between social conditions linked to low COVID-19 vaccination rates. Understanding how neighborhood conditions combine to create inequities in communities could inform the design of interventions tailored to address COVID-19 vaccination disparities.

Project description:BackgroundPreterm birth continues to be a major public health problem contributing to 75% of the neonatal mortality worldwide. Low birth weight (LBW) is an important but imperfect surrogate for prematurity when accurate assessment of gestational age is not possible. While there is overlap between preterm birth and LBW newborns, those that are both premature and LBW are at the highest risk of adverse neonatal outcomes. Understanding the epidemiology of preterm birth and LBW is important for prevention and improved care for at risk newborns, but in many countries, data are sparse and incomplete.MethodsWe conducted data analyses using the Global Network's (GN) population-based registry of pregnant women and their babies in rural communities in six low- and middle-income countries (Democratic Republic of Congo, Kenya, Zambia, Guatemala, India and Pakistan). We analyzed data from January 2014 to December 2018. Trained study staff enrolled all pregnant women in the study catchment area as early as possible during pregnancy and conducted follow-up visits shortly after delivery and at 42 days after delivery. We analyzed the rates of preterm birth, LBW and the combination of preterm birth and LBW and studied risk factors associated with these outcomes across the GN sites.ResultsA total of 272,192 live births were included in the analysis. The overall preterm birth rate was 12.6% (ranging from 8.6% in Belagavi, India to 21.8% in the Pakistani site). The overall LBW rate was 13.6% (ranging from 2.7% in the Kenyan site to 21.4% in the Pakistani site). The overall rate of both preterm birth and LBW was 5.5% (ranging from 1.2% in the Kenyan site to 11.0% in the Pakistani site). Risk factors associated with preterm birth, LBW and the combination were similar across sites and included nulliparity [RR - 1.27 (95% CI 1.21-1.33)], maternal age under 20 [RR 1.41 (95% CI 1.32-1.49)] years, severe antenatal hemorrhage [RR 5.18 95% CI 4.44-6.04)], hypertensive disorders [RR 2.74 (95% CI - 1.21-1.33], and 1-3 antenatal visits versus four or more [RR 1.68 (95% CI 1.55-1.83)].ConclusionsPreterm birth, LBW and their combination continue to be common public health problems at some of the GN sites, particularly among young, nulliparous women who have received limited antenatal care services. Trial registration The identifier of the Maternal and Newborn Health Registry at ClinicalTrials.gov is NCT01073475.Trial registrationThe identifier of the Maternal and Newborn Health Registry at ClinicalTrials.gov is NCT01073475.

Project description:BackgroundThere are concerns about the degree to which county income level might underlie the stark disparities in cancer death rates among US counties; at the same time, there is uncertainty about the factors that may mediate the disparities.ObjectivesTo assess county-level cancer death rates and to identify possible mediators of the association between county-level median incomes and cancer death rates.Design, setting, and participantsCross-sectional study using death records from the National Center for Health Statistics in 2014, with data collected and analyzed between October 1, 2016, and July 31, 2017. All US counties and county equivalents were included.ExposuresCounty-level median household income.Main outcomes and measuresCounty-level age-standardized cancer death rate.ResultsIn 3135 counties, median incomes ranged from $22?126 to $121?250 per year. Low-income counties (median income, $33?445) vs high-income counties (median income, $55?780) had higher proportions of residents who were non-Hispanic black, lived in rural areas, or reported poor or fair health. The mean (SD) cancer death rate was 185.9 (24.4) per 100?000 person-years in high-income counties, compared with 204.9 (26.3) and 229.7 (32.9) per 100?000 person-years in medium- and low-income counties, respectively. In mediation models, health risk behaviors (smoking, obesity, and physical inactivity); clinical care factors (unaffordable care and low-quality care); health environments (food insecurity); and health policies (state smoke-free laws and Medicaid payment rates) in aggregate accounted for more than 80% of the income-related disparity. The strongest possible mediators were food insecurity (explaining 19.1% [95% CI, 12.5%-26.5%] of the association between county incomes and cancer deaths), low-quality care (17.9%; 95% CI, 14.0%-21.8%), smoking (12.7%; 95% CI, 9.4%-15.6%), and physical inactivity (12.2%; 95% CI, 9.4%-15.6%).Conclusions and relevanceThere are wide gaps in cancer death rates between low-, medium-, and high-income counties. Future studies are needed to assess whether targeting the possible mediators might ameliorate the substantial socioeconomic cancer disparities.

Project description:BackgroundLow birthweight (LBW) (< 2500 g) is a significant determinant of infant morbidity and mortality worldwide. In low-income settings, the quality of birthweight data suffers from measurement and recording errors, inconsistent data reporting systems, and missing data from non-facility births. This paper describes birthweight data quality and the prevalence of LBW before and after implementation of a birthweight quality improvement (QI) initiative in Amhara region, Ethiopia.MethodsA comparative pre-post study was performed in selected rural health facilities located in West Gojjam and South Gondar zones. At baseline, a retrospective review of delivery records from February to May 2018 was performed in 14 health centers to collect birthweight data. A birthweight QI initiative was introduced in August 2019, which included provision of high-quality digital infant weight scales (precision 5 g), routine calibration, training in birth weighing and data recording, and routine field supervision. After the QI implementation, birthweight data were prospectively collected from late August to early September 2019, and December 2019 to June 2020. Data quality, as measured by heaping (weights at exact multiples of 500 g) and rounding to the nearest 100 g, and the prevalence of LBW were calculated before and after QI implementation.ResultsWe retrospectively reviewed 1383 delivery records before the QI implementation and prospectively measured 1371 newborn weights after QI implementation. Heaping was most frequently observed at 3000 g and declined from 26% pre-initiative to 6.7% post-initiative. Heaping at 2500 g decreased from 5.4% pre-QI to 2.2% post-QI. The percentage of rounding to the nearest 100 g was reduced from 100% pre-initiative to 36.5% post-initiative. Before the QI initiative, the prevalence of recognized LBW was 2.2% (95% confidence interval [CI]: 1.5-3.1) and after the QI initiative increased to 11.7% (95% CI: 10.1-13.5).ConclusionsA QI intervention can improve the quality of birthweight measurements, and data measurement quality may substantially affect estimates of LBW prevalence.

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Geographic Hotspots for Low Birthweight: An Analysis of Counties With Persistently High Rates.

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Geographic Hotspots for Low Birthweight: An Analysis of Counties With Persistently High Rates.

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