Social determinants and inequitable maternal and perinatal outcomes in Aotearoa New Zealand.

OBJECTIVES: Aotearoa New Zealand has demonstrable maternal and perinatal health inequity. We examined the relationships between adverse outcomes in a total population sample of births and a range of social determinant variables representing barriers to equity. METHODS: Using the Statistics New Zealand Integrated Data Infrastructure suite of linked administrative data sets, adverse maternal and perinatal outcomes (mortality and severe morbidity) were linked to socio-economic and health variables for 97% of births in New Zealand between 2003 and 2018 (~970,000 births). Variables included housing, economic, health, crime and family circumstances. Logistic regression examined the relationships between adverse outcomes and social determinants, adjusting for demographics (socio-economic deprivation, education, parity, age, rural/urban residence and ethnicity). RESULTS: Maori (adjusted odds ratio = 1.21, 95% confidence interval = 1.18-1.23) and Asian women (adjusted odds ratio 1.39, 95% confidence interval = 1.36-1.43) had poorer maternal or perinatal outcomes compared to New Zealand European/European women. High use of emergency department (adjusted odds ratio = 2.68, 95% confidence interval = 2.53-2.84), disability (adjusted odds ratio = 1.98, 95% confidence interval = 1.83-2.14) and lack of engagement with maternity care (adjusted odds ratio = 1.89, 95% confidence interval = 1.84-1.95) had the strongest relationship with poor outcomes. CONCLUSION: Maternal health inequity was strongly associated with a range of socio-economic and health determinants. While some of these factors can be targeted for interventions, the study highlights larger structural and systemic issues that affect maternal and perinatal health.

A whole of population retrospective observational study on the rates of polypharmacy in New Zealand 2014 to 2018 Polypharmacy in New Zealand: What is the current status?

BACKGROUND AND AIMS: Polypharmacy (≥5 medicines) and hyperpolypharmacy (≥10 medicines) can significantly impact people's health. The literature surrounding polypharmacy focuses on the elderly, particularly rest home populations, with few studies looking into younger age bands. Moreover, there have been no recent studies looking into the rates of polypharmacy in New Zealand. This study aimed to determine whether polypharmacy rates have increased over time in the New Zealand population. Specifically investigating polypharmacy rates across age and ethnicity, and identifying which medicines are most commonly prescribed in people with polypharmacy. METHODS: A nationwide retrospective observational study was carried out between 2014 and 2018 on 4 697 274 New Zealanders (96% of the population) by linking dispensing data from the Pharmaceutical Collection to patient enrolment data using a National Health Identifier (NHI) to identify the rate of long-term medicine prescribing in New Zealand. RESULTS: Our study found the rate of polypharmacy to be 9.93% and hyperpolypharmacy to be 1.92% nationwide in 2018, a percentage increase of 4.1% and 7.11% from 2014, respectively. During the same period, we observed the greatest percentage increase (30.37%) in the rate of polypharmacy in the 20 to 29 age band while the rates decreased in older populations. Variation was also noted between ethnicities. Medicines contributing to polypharmacy differed by age group. CONCLUSION: Current methods for minimizing polypharmacy and optimizing medicines use are narrowly focused on the elderly. Despite an increase in education and awareness raising campaigns, rates continue to rise in New Zealand's population.

Do maternity services in New Zealand's public healthcare system deliver on equity? Findings from structural equation modelling of national maternal satisfaction survey data.

BACKGROUND: Birthing outcomes in New Zealand are demonstrably inequitable based on governmental reports and research. However, the last Ministry of Health maternal satisfaction survey in 2014 indicated that 77% of women were satisfied or very satisfied with care. This study used data from the maternal satisfaction survey to examine aspects of inequity in reported satisfaction with care. METHODS: Structural Equation Modelling (SEM) was used to infer latent variables of satisfaction with equity domains from responses to the satisfaction survey. Additional data (residential location and deprivation score), not used in the Ministry of Health primary analysis, were provided and included in this modelling. RESULTS: SEM showed that satisfaction was not equitably distributed. Younger women, those from areas of high socio-economic deprivation, and remote rural women were most likely to be affected by dissatisfaction associated with physical access, cultural care, information provided, and/or barriers to equity associated with additional costs (all p<0.05). Financial burden of additional costs was also unevenly distributed. CONCLUSION: While these findings are congruent with other research on the association between social determinants and maternal satisfaction, it is concerning that they remain sources of inequity in New Zealand twenty years after they were first identified as priorities to address. On the basis of this study, urgent attention needs to be paid to removing sources of inequity within the health system and maternity care in particular.

Estimation of linkage disequilibrium and effective population size in New Zealand sheep using three different methods to create genetic maps.

BACKGROUND: Investments in genetic selection have played a major role in the New Zealand sheep industry competitiveness. Selection may erode genetic diversity, which is a crucial factor for the success of breeding programs. Better understanding of linkage disequilibrium (LD) and ancestral effective population size (Ne) through quantifying this diversity and comparison between populations allows for more informed decisions with regards to selective breeding taking population genetic diversity into account. The estimation of N e can be determined via genetic markers and requires knowledge of genetic distances between these markers. Single nucleotide polymorphisms (SNP) data from a sample of 12,597 New Zealand crossbred and purebred sheep genotyped with the Illumina Ovine SNP50 BeadChip was used to perform a genome-wide scan of LD and N e . Three methods to estimate genetic distances were investigated: 1) M1: a ratio fixed across the whole genome of one Megabase per centiMorgan; 2) M2: the ratios of genetic distance (using M3, below) over physical distance fixed for each chromosome; and, 3) M3: a genetic map of inter-SNP distances estimated using CRIMAP software (v2.503). RESULTS: The estimates obtained with M2 and M3 showed much less variability between autosomes than those with M1, which tended to give lower N e results and higher LD decay. The results suggest that N e has decreased since the development of sheep breeds in Europe and this reduction in Ne has been accelerated in the last three decades. The N e estimated for five generations in the past ranged from 71 to 237 for Texel and Romney breeds, respectively. A low level of genetic kinship and inbreeding was estimated in those breeds suggesting avoidance of mating close relatives. CONCLUSIONS: M3 was considered the most accurate method to create genetic maps for the estimation of LD and Ne. The findings of this study highlight the history of genetic selection in New Zealand crossbred and purebred sheep and these results will be very useful to understand genetic diversity of the population with respect to genetic selection. In addition, it will help geneticists to identify genomic regions which have been preferentially selected within a variety of breeds and populations.

Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.

BACKGROUND: Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs). RESULTS: Imputation from 5K to HD was slightly better (0.6 %) than imputation from 5K to 50K. Two-step imputation from 5K to 50K and then from 50K to HD outperformed direct imputation from 5K to HD. A slight loss in imputation accuracy was observed when a large fixed reference population was used compared to a smaller within-breed reference (including all 50K genotypes on animals from different breeds excluding those in the validation set i.e. to be imputed), but only for a few animals across all imputation scenarios from 5K to 50K. However, a major gain in imputation accuracy for a large proportion of animals (purebred and crossbred), justified the use of a fixed and large reference dataset for all situations. This study also investigated the loss in imputation accuracy specifically for SNPs located at the ends of each chromosome, and showed that only chromosome 26 had an overall imputation (5K to 50K) accuracy for 100 SNPs at each end higher than 60 % (r2). Most of the chromosomes displayed reduced imputation accuracy at least at one of their ends. Prediction of imputation accuracy based on the relatedness of low-density genotypes to those of the reference dataset, before imputation (without running an imputation software) was also investigated. FIMPUTE V2.2 outperformed BEAGLE 3.3.2 across all imputation scenarios. CONCLUSIONS: Imputation accuracy in sheep breeds can be improved by following a set of recommendations on SNP panels, software, strategies of imputation (one- or two-step imputation), and choice of the animals to be genotyped using both high- and low-density SNP panels. We present a method that predicts imputation accuracy for individual animals at the low-density level, before running imputation, which can be used to restrict genomic prediction only to the animals that can be imputed with sufficient accuracy.

Copy number variants in the sheep genome detected using multiple approaches.

BACKGROUND: Copy number variants (CNVs) are a type of polymorphism found to underlie phenotypic variation, both in humans and livestock. Most surveys of CNV in livestock have been conducted in the cattle genome, and often utilise only a single approach for the detection of copy number differences. Here we performed a study of CNV in sheep, using multiple methods to identify and characterise copy number changes. Comprehensive information from small pedigrees (trios) was collected using multiple platforms (array CGH, SNP chip and whole genome sequence data), with these data then analysed via multiple approaches to identify and verify CNVs. RESULTS: In total, 3,488 autosomal CNV regions (CNVRs) were identified in this study, which substantially builds on an initial survey of the sheep genome that identified 135 CNVRs. The average length of the identified CNVRs was 19 kb (range of 1 kb to 3.6 Mb), with shorter CNVRs being more frequent than longer CNVRs. The total length of all CNVRs was 67.6Mbps, which equates to 2.7 % of the sheep autosomes. For individuals this value ranged from 0.24 to 0.55 %, and the majority of CNVRs were identified in single animals. Rather than being uniformly distributed throughout the genome, CNVRs tended to be clustered. Application of three independent approaches for CNVR detection facilitated a comparison of validation rates. CNVs identified on the Roche-NimbleGen 2.1M CGH array generally had low validation rates with lower density arrays, while whole genome sequence data had the highest validation rate (>60 %). CONCLUSIONS: This study represents the first comprehensive survey of the distribution, prevalence and characteristics of CNVR in sheep. Multiple approaches were used to detect CNV regions and it appears that the best method for verifying CNVR on a large scale involves using a combination of detection methodologies. The characteristics of the 3,488 autosomal CNV regions identified in this study are comparable to other CNV regions reported in the literature and provide a valuable and sizeable addition to the small subset of published sheep CNVs.

Genomic prediction and genome-wide association study for dagginess and host internal parasite resistance in New Zealand sheep.

BACKGROUND: Dagginess (faecal soiling of the perineum region) and host nematode parasite resistance are important animal welfare traits in New Zealand sheep. Genomic prediction (GP) estimates the genetic merit, as a molecular breeding value (mBV), for each trait based on many SNPs. The additional information the mBV provides (as determined by its accuracy) has led to its incorporation into breeding schemes. Some GP methods give SNP effects, which provide additional information to identify genome-wide associations (GWAS) for a trait of interest. Here we report results from a GP and GWAS study for dagginess and host nematode parasite resistance in a New Zealand sheep industry resource. RESULTS: Genomic prediction analysis was performed using 50K SNP chip data and parent average-removed, de-regressed BVs for five traits, from a resource of 8705 pedigree recorded animals. The five traits were dag score at three and eight months (DAG3, DAG8) and nematode faecal egg count in summer (FEC1), autumn (FEC2) and as an adult (AFEC). The resource consisted of Romney, Coopworth, Perendale, Texel and various breed crosses (designated: CompRCP, CompRCPT and CompCRP). The pure breeds, apart from Texel, plus CompRCP were used to develop the GP. The resulting SNP effects were used to identify genetic regions associated with dagginess and parasite resistance. Accuracies of the weighted correlation between mBV and true BV ranged between -0.07 (Texel) and 0.56 (Coopworth) for DAG3 and DAG8. For FEC1, FEC2 and AFEC accuracies ranged between -0.22 (CompRCPT) and 0.69 (Coopworth). The weighted average individual accuracy (calculated from theory) ranges were 0.13 (Texel) to 0.52 (Coopworth) and 0.11 (Texel) to 0.55 (Coopworth) respectively, for dagginess and parasite traits. There was one SNP for DAG8 that reached Bonferroni significance threshold (P < 1 × 10(-6)) on OAR15, the same two SNPs for each of the parasite traits (OAR26) and none for DAG3. A notable peak was also observed on OAR7 for all the parasite traits, however, it did not reach the Bonferroni significance threshold. CONCLUSIONS: This study presents the first results of a GWAS on dagginess and faecal egg count traits in New Zealand sheep. The results suggest that there are quantitative trait loci on OAR 15 for dagginess and on OAR26 and seven for faecal egg count.

Genomic breed prediction in New Zealand sheep.

BACKGROUND: Two genetic marker-based methods are compared for use in breed prediction, using a New Zealand sheep resource. The methods were a genomic selection (GS) method, using genomic BLUP, and a regression method (Regp) using the allele frequencies estimated from a subset of purebred animals. Four breed proportions, Romney, Coopworth, Perendale and Texel, were predicted, using Illumina OvineSNP50 genotypes. RESULTS: Both methods worked well with correlations of predicted proportions and recorded proportions ranging between 0.91 and 0.97 across methods and prediction breeds, except for the Regp method for Perendales, where the correlation was 0.85. The Regp method gives predictions that appear as a gradient (when viewed as the first few principal components of the genomic relatedness matrix), decreasing away from the breed centre. In contrast the GS method gives predictions dominated by the breeds of the closest relatives in the training set. Some Romneys appear close to the main Perendale group, which is why the Regp method worked less well for predicting Perendale proportion. The GS method works better than the Regp method when the breed groups do not form tight, distinct clusters, but is less robust to breed errors in the training set (for predicting relatives of those animals). Predictions were found to be similar to those obtained using STRUCTURE software, especially those using Regp. The methods appear to overpredict breed proportions in animals that are far removed from the training set. It is suggested that the training set should include animals spanning the range where predictions are made. CONCLUSIONS: Breeds can be predicted using either of the two methods investigated. The choice of method will depend on the structure of the breeds in the population. The use of genomic selection methodology for breed prediction appears promising. As applied, it worked well for predicting proportions in animals that were predominantly of the breed types present in the training set, or to put it another way, that were in the range of genetic diversity represented by the training set. Therefore, it would be advisable that the training set covered the breed diversity of where predictions will be made.

Using genetic markers in unpedigreed populations to detect a heritable trait.

Before a breeder invests selection pressure on a trait of interest, it needs to be established whether that trait is actually heritable. Some traits may not have been measured widely in pedigreed populations, for example, a disease or deformity may become more prevalent than previously, but is still relatively rare. One approach to detect inheritance would be to screen a commercial population to obtain a sample of "affecteds" (the test group) and to also obtain a random control group. These individuals are then genotyped with a set of genetic markers and the relationships between individuals within each group estimated. If the relatedness is higher in the test group than in the control group, this provides initial evidence for the trait being heritable. A power simulation shows that this approach is feasible with moderate resources.

Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals.

This paper presents a quick, easy to implement and versatile way of using stochastic simulations to investigate the power and design of using single nucleotide polymorphism (SNP) arrays for genome-wide association studies in farm animals. It illustrates the methodology by discussing a small example where 6 experimental designs are considered to analyse the same resource consisting of 6,006 animals with pedigree and phenotypic records: (1) genotyping the 30 most widely used sires in the population and all of their progeny (515 animals in total), (2) genotyping the 100 most widely used sires in the population and all of their progeny (1,102 animals in total), genotyping respectively (3) 515 and (4) 1,102 animals selected randomly or genotyping respectively (5) 515 and (6) 1,102 animals from the tails of the phenotypic distribution. Given the resource at hand, designs where the extreme animals are genotyped perform the best, followed by designs selecting animals at random. Designs where sires and their progeny are genotyped perform the worst, as even genotyping the 100 most widely used sires and their progeny is not as powerful of genotyping 515 extreme animals.