Increases in obstetric interventions and changes in gestational age distributions of U.S. births

Objective: To examine how changes in induction of labor (IOL) and cesarean deliveries between 1990 and 2017 affected gestational age distributions of births in the United States. Materials and Methods: Singleton first births were drawn from the National Vital Statistics System Birth Data for years 1990–2017. Separate analytic samples were created (1) by maternal race/ethnicity (Hispanic, non-Hispanic Black, non-Hispanic Asian, and non-Hispanic white), (2) by maternal age (15–19, 20–24, 25–29, 30–34, 35–39, 40–49), (3) by U.S. states, and (4) for women at low risk for obstetric interventions (e.g., age 20–34, no hypertension, no diabetes, no tobacco use). Gestational age was measured in weeks, and obstetric intervention status was measured as: (1) no IOL, vaginal delivery; (2) no IOL, cesarean delivery; and (3) IOL, all deliveries. The joint probabilities of birth at each gestational week by obstetric intervention status for years 1990–1991, 1998–1999, 2007–2008, and 2016–2017 were estimated. Results: Between 1990 and 2017, the percent of singleton first births occurring between 37 and 39 weeks of gestation increased from 38.5% to 49.5%. The changes were driven by increases in IOL and a shift in the use of cesarean deliveries toward earlier gestations. The changes were observed among all racial/ethnic groups and all maternal ages, and across all U.S. states. The same changes were also observed among U.S. women at low risk for interventions. Conclusion: Changes in gestational age distributions of U.S. births and their underlying causes are likely national-level phenomena and do not appear to be responding to increases in maternal risk for interventions

Differences in determinants: racialized obstetric care and increases in U.S. state labor induction rates

Induction of labor (IOL) rates in the United States have nearly tripled since 1990. We examine official U.S. birth records to document increases in states’ IOL rates among pregnancies to Black, Latina, and White women. We test if the increases are associated with changes in demographic characteristics and risk factors among states’ racial-ethnic childbearing populations. Among pregnancies to White women, increases in state IOL rates are strongly associated with changes in risk factors among White childbearing populations. However, the rising IOL rates among pregnancies to Black and Latina women are not due to changing factors in their own populations but are instead driven by changing factors among states’ White childbearing populations. The results suggest systemic racism may be shaping U.S. obstetric care whereby care is not “centered at the margins” but is instead responsive to characteristics in states’ White populations

Fitting Age-Period-Cohort Models Using the Intrinsic Estimator: Assumptions and Misapplications

We thank Demography’s editorial office for the opportunity to respond to te Grotenhuis et al.’s commentary regarding the methods used and the results presented in our earlier paper (Masters et al. 2014). In this response, we briefly reply to three general themes raised in the commentary: (1) the presentation and discussion of APC results, (2) the fitting of full APC models to data for which a simpler model holds, and (3) the variation in the estimated age, period, and cohort coefficients produced by the intrinsic estimator (IE) (i.e., the “non-uniqueness property” of the IE, as referred to by Pelzer et al. (2015))

Should age-period-cohort studies return to the methodologies of the 1970s?

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods – hierarchical APC (HAPC) modeling – to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question – along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that “solid theory” is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies

The Impact of Obesity on US Mortality Levels: The Importance of Age and Cohort Factors in Population Estimates

To estimate the percentage of excess death for US Black and White men and women associated with high body mass, we examined the combined effects of age variation in the obesity–mortality relationship and cohort variation in age-specific obesity prevalence

Clarifying hierarchical age–period–cohort models: A rejoinder to Bell and Jones

Previously, Reither et al. (2015) demonstrated that hierarchical age–period–cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide “misleading evidence dressed up as science.” Despite such strong words, B&J show no curiosity about their own simulated data—and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the “true” DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy—testing HAPC models with data simulated from contrived DGPs that violate important assumptions—is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences

The cosmological significance of Low Surface Brightness galaxies found in a deep blind neutral-hydrogen survey

We have placed limits on the cosmological significance of gas-rich low surface-brightness (LSB) galaxies as a proportion of the total population of gas-rich galaxies by carrying out a very deep survey (HIDEEP) for neutral hydrogen (HI) with the Parkes multibeam system. Such a survey avoids the surface-brightness selection effects that limit the usefulness of optical surveys for finding LSB galaxies. To complement the HIDEEP survey we have digitally stacked eight 1-hour R-band Tech Pan films from the UK Schmidt Telescope covering 36 square degrees of the survey area to reach a very deep isophotal limit of 26.5 R mag/sq. arcsec. At this level, we find that all of the 129 HI sources within this area have optical counterparts and that 107 of them can be identified with individual galaxies. We have used the properties of the galaxies identified as the optical counterparts of the HI sources to estimate the significance of LSB galaxies (defined to be those at least 1.5 magnitudes dimmer in effective surface-brightness than the peak in the observed distribution seen in optical surveys). We calculate the contribution of LSB galaxies to the total number, neutral hydrogen density, luminosity density, baryonic mass density, dynamical mass density and cross-sectional area of gas-rich galaxies. We do not find any `Crouching Giant' LSB galaxies such as Malin 1, nor do we find a population of extremely low surface-brightness galaxies not previously found by optical surveys. Such objects must either be rare, gas-poor or outside the survey detection limits.Comment: 13 pages, 14 figures. Accepted for publication in MNRA

Agribusiness Sheep Updates - 2004 part 2

Precision Pastures Using Species Diversity to Improve Pasture Performance Anyou Liu and Clinton Revell, Department of Agriculture, Western Australia New Annual Pasture Legumes for Sheep Graziers Phil Nichols, Angelo Loi, Brad Nutt and Darryl McClements Department of Agriculture Western Australia Pastures from Space – Can Satellite Estimates of Pasture Growth Rate be used to Increase Farm Profit? Lucy Anderton, Stephen Gherardi and Chris Oldham Department of Agriculture Western Australia Summer-active Perennial Grasses for Profitable Sheep Production Paul Sanford and John Gladman, Department of Agriculture, Western Australia Pastures From Space – Validation Of Predictions Of Pasture Growth Rates DONALD, G.E.A, EDIRISINGHE, A.A, HENRY, D.A.A, MATA, G.A, GHERARDI, S.G.B, OLDHAM, C.M.B, GITTINS, S.P.B AND SMITH, R. C. G.C ACSIRO, Livestock Industries, PMB 5, Wembley, WA, 6913. BDepartment of Agriculture Western Australia, Bentley, WA, 6983. C Department of Land Information Western Australia, Floreat, WA, 6214. Production and Management of Biserrula Pasture - Managing the Risk of Photosensitivity Dr Clinton Revell and Roy Butler, Department of Agriculture Western Australia Meat Quality of Sheep Grazed on a Saltbush-based Pasture Kelly Pearce1,2, David Masters1, David Pethick2, 1 CSIRO LIVESTOCK INDUSTRIES, WEMBLEY, WA 2 SCHOOL OF VETERINARY AND BIOMEDICAL SCIENCE, MURDOCH UNIVERSITY, MURDOCH, WA Precision Sheep Lifetime Wool – Carryover Effects on Subsequent Reproduction of the Ewe Flock Chris Oldham, Department of Agriculture Western Australia Andrew Thompson, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Ewe Productivity Trials - a Linked Analysis Ken Hart, Johan Greeff, Department of Agriculture Western Australia, Beth Paganoni, School of Animal Biology, Faculty of Natural and Agricultural Sciences, University of Western Australia. Grain Finishing Systems For Prime Lambs Rachel Kirby, Matt Ryan, Kira Buttler, Department of Agriculture, Western Australia The Effects of Nutrition and Genotype on the Growth and Development, Muscle Biochemistry and Consumer Response to Lamb Meat David Pethick, Department of Veterinary Science, Murdoch University, WA, Roger Heggarty and David Hopkins, New South Wales Agriculture ‘Lifetime Wool’ - Effects of Nutrition During Pregnancy and Lactation on Mortality of Progeny to Hogget Shearing Samantha Giles, Beth Paganoni and Tom Plaisted, Department of Agriculture Western Australia, Mark Ferguson and Darren Gordon, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Lifetime Wool - Target Liveweights for the Ewe Flock J. Young, Farming Systems Analysis Service, Kojonup, C. Oldham, Department of Agriculture Western Australia, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC Lifetime Wool - Effects of Nutrition During Pregnancy and Lactation on the Growth and Wool Production of their Progeny at Hogget Shearing B. Paganoni, University of Western Australia, Nedlands WA, C. Oldham, Department of Agriculture Western Australia, M. Ferguson, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC RFID Technology – Esperance Experiences Sandra Brown, Department of Agriculture Western Australia The Role of Radio Frequency Identification (RFID) Technology in Prime Lamb Production - a Case Study. Ian McFarland, Department of Agriculture, Western Australia. John Archer, Producer, Narrogin, Western Australia Win with Twins from Merinos John Milton, Rob Davidson, Graeme Martin and David Lindsay The University of Western Australia Precision Sheep Need Precision Wool Harvesters Jonathan England, Castle Carrock Merinos, Kingston SE, South Australia Business EBVs and Indexes – Genetic Tools for your Toolbox Sandra Brown, Department of Agriculture Western Australia Green Feed Budget Paddock Calculator Mandy Curnow, Department of Agriculture Western Australia Minimising the Impact of Drought - Evaluating Flock Recovery Options using the ImPack Model Karina P. Wood, Ashley K. White, B. Lloyd Davies, Paul M. Carberry, NSW Department of Primary Industries (NSW DPI), Lifetime Wool - Modifying GrazFeed® for WA Mike Hyder, Department of Agriculture Western Australia , Mike Freer, CSIRO Plant Industry, Canberra, A.C.T. , Andrew van Burgel, and Kazue Tanaka, Department of Agriculture Western Australia Profile Calculator – A Way to Manage Fibre Diameter Throughout the Year to Maximise Returns Andrew Peterson, Department of Agriculture, Western Australia Pasture Watch - a Farmer Friendly Tool for Downloading and Analysing Pastures from Space Data Roger Wiese,Fairport Technologies International, South Perth, WA, Stephen Gherardi, BDepartment of Agriculture Western Australia, Gonzalo Mata, CCSIRO, Livestock Industries, Wembley, Western Australia, and Chris Oldham, Department of Agriculture Western Australia Sy Sheep Cropping Systems An Analysis of a Cropping System Containing Sheep in a Low Rainfall Livestock System. Evan Burt, Amanda Miller, Anne Bennett, Department of Agriculture, Western Australia Lucerne-based Pasture for the Central Wheatbelt – is it Good Economics? Felicity FluggeA, Amir AbadiA,B and Perry DollingA,B,A CRC for Plant-based Management of Dryland Salinity: BDept. of Agriculture, WA Sheep and Biserrula can Control Annual Ryegrass Dean Thomas, John Milton, Mike Ewing and David Lindsay, The University of WA, Clinton Revell, Department of Agriculture, Western Australia Sustainable Management Pasture Utilisation, Fleece Weight and Weaning Rate are Integral to the Profitability of Dohnes and SAMMs. Emma Kopke,Department of Agriculture Western Australia, John Young, Farming Systems Analysis Service Environmental Impact of Sheep Confinement Feeding Systems E A Dowling and E K Crossley, Department of Agriculture, Western Australia Smart Grazing Management for Production and Environmental Outcomes Dr Brien E (Ben) Norton, Centre for the Management of Arid Environments, Curtin University of Technology, WA Common Causes of Plant Poisoning in the Eastern Wheatbelt of Western Australia. Roy Butler, Department of Agriculture, Western Australia Selecting Sheep for Resistance to Worms and Production Trait Responses John Karlsson, Johan Greeff, Department of Agriculture, Western Australia, Geoff Pollott, Imperial College, London UK Production and Water Use of Lucerne and French Serradella in Four Soil Types, Diana Fedorenko1,4, Darryl McClements2,4 and Robert Beard3,4, 12Department of Agriculture, Western Australia; 3Farmer, Meckering; 4CRC for Plant-based Management of Dryland Salinity. Worm Burdens in Sheep at Slaughter Brown Besier, Department of Agriculture Western Australia, Una Ryan, Caroline Bath, Murdoch Universit

Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (P) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD