2006 new south whales australia public vital records


NSW Perinatal Data Collection (PDC - formerly the Midwives Data Collection, MDC)

Stillbirth is relatively understudied and the deaths have not been accounted for globally as part of other major maternal and child health strategies [ 2 ]. In Australia 1 in every babies are stillborn which represented families in [ 3 ]. The increasing prevalence of these population risk factors has been postulated to contribute to the lack of any significant decrease in stillbirth rates in developed country settings over the past 10 — 20 years [ 5 ].

Background

As with other high income settings, the number of women delaying childbearing in Australia continues to increase. The mean maternal age is Smoking during pregnancy in NSW has declined but remains at Obesity is an increasing problem in pregnant women contributing not only to other pregnancy risks such as hypertension but also independently to stillbirth [ 7 , 8 ].

Both smoking and obesity are risk factors modifiable by women who are planning a pregnancy however maternal age is not and the underlying mechanism of the age-associated risk remains unclear. A recent systematic review of maternal age and risk of stillbirth was unable to pool studies to determine the magnitude of stillbirth risk due to significant statistical heterogeneity [ 11 ]. Also, few of the included studies analysed antepartum stillbirths before labour separately to intrapartum stillbirths during labour , a significant omission as the underlying causes, the associated risk factors and the suggested denominators for statistical analysis differ [ 12 — 14 ].

Although awareness of increased risks of adverse pregnancy outcome may contribute over time to women having children earlier there is a need to be able to give accurate risks to older women who are currently pregnant or planning a pregnancy. Two very large recent studies in the US and Norway have assessed risk of stillbirth per ongoing pregnancy by gestational age week for different maternal age groups however neither was able to separate antepartum from intrapartum deaths or provide any cause of death information [ 15 , 16 ].

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Despite known risk factors for stillbirth many of these deaths remain unexplained and there is an urgent need to identify and target appropriate areas for research and prevention of stillbirth. Not knowing why a baby died makes it difficult for health providers to counsel regarding future pregnancy management and recurrence risk and potentially magnifies the grief and bereavement outcomes for the family. Unexplained stillbirths have been shown in an analysis of stillbirths from Canada to be the only type of stillbirths statistically more common in older women [ 18 ] however the majority of studies assessing maternal age and stillbirth do not include information of cause of death [ 11 ].

One study addressing uteroplacental insufficiency and its association with advanced maternal age showed no difference in the stillbirths with fetal growth restriction between older and younger women [ 19 ]. The purpose of this study was to determine the most important current risk factors for antepartum stillbirth in NSW Australia and to quantify the risk of advanced maternal age by gestation in order to better inform older mothers and maternity care providers, particularly regarding risks at the end of pregnancy. Additionally we aimed to assess whether the classification of cause of death differed for stillbirths in older women.

The New South Wales Midwives Data Collection is a mandated population-based surveillance system covering all births in NSW public and private hospitals, as well as home births [ 6 ]. It encompasses all livebirths and stillbirths of at least 20 weeks gestation or at least grams birth weight. The Midwives Data Collection requires the attending midwife or doctor to complete a notification form when a birth occurs, and collects demographic, maternal health, pregnancy, labour, delivery, and perinatal outcomes data Additional file 1 : Appendix S1. The NSW Maternal and Perinatal Committee is a quality assurance committee established under the NSW Health Administration Act , and is privileged under this Act to carry out confidential reviews of both maternal and perinatal deaths [ 6 ].

Members are appointed by the Minister for Health. Information available to the working party at review is forwarded by hospitals and includes a confidential report on perinatal death Additional file 2 : Appendix S2 , post mortem and placental pathology reports as well as any other information considered relevant by the local hospital perinatal death review committee. Information considered by the Committee is confidential. Gestational age was determined by certain dates confirmed by ultrasound before 20 weeks gestation or if dates uncertain by ultrasound alone prior to 20 weeks or if unavailable by examination of the newborn infant.

Stillbirth was defined as a baby born of at least 20 weeks gestation or g birthweight who did not, at any time after delivery, breathe, or show any evidence of life such as a heartbeat. Antepartum stillbirth was defined as death before the onset of labour from the Perinatal Death Database or induction of labour for known fetal death from the Midwives Data Collection.

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Unexplained antepartum death was defined as the death of a normally formed fetus prior to the onset of labour where no predisposing factors are considered likely to have caused the death. Babies were included if they were at least 22 weeks and less than 43 weeks gestation. Cause of Death classification was extracted from the Perinatal Death Database on all stillborn infants that had been reviewed by the Perinatal Outcomes Working Party. This category includes deaths where a congenital abnormality structural, functional or chromosomal is considered to have made a major contribution to the death and includes terminations of pregnancy for congenital abnormality.

Demographic data were collected from both datasets. Rates of antepartum stillbirth were computed by dividing the number of stillbirths by the sum of live births and stillbirths and multiplying by The risk of stillbirth was compared between groups by time to event analyses using gestation as the time scale and antepartum stillbirth as the event. All other births were censored. Crude and adjusted hazard ratios were estimated using a Cox proportional hazards model. We used Wald tests to test the effect of each covariate and those with P-values less than 0.

Only first birth within the time period was included to avoid intraperson correlation. The proportional hazards assumption was tested using Schoenfeld residuals which were plotted against each covariate and the graphs inspected for any trend in the residuals [ 22 , 23 ]. Interactions between the log of gestational age and each covariate were created and added to the model individually to test for any departure from the proportional hazards assumption [ 24 ]. Records with missing data for the variables analysed were excluded.

We calculated the risk of still birth at each week of gestation by dividing the number of still births that occurred during that week by the total number of ongoing pregnancies at the beginning of the week minus half of the number of live births in that week. All births were included in the calculation of these risks. Statistical analysis was performed using SAS version 9. There were a total of singleton births of at least 22 weeks gestation in NSW over the five year study period excluding deaths due to fetal anomaly.


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Using only first birth within the time period there were births and antepartum stillbirths. Overall stillbirth rates are presented in Table 1. Crude and adjusted hazard ratios HR for antepartum stillbirth are presented in Table 2. Risk factors significantly associated with antepartum stillbirth on adjusted analyses were: maternal age, smoking, nulliparity, pre-existing hypertension, pre-existing diabetes, area health service of residence and country of birth. Maternal age showed a dose—response relationship with a HR 1. Smoking conferred an increased risk of 1.

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Area health service of residence and country of birth were used as available surrogate markers of socioeconomic status. Importantly, after adjusting for the other variables indigenous status was not a significant risk factor for antepartum stillbirth HR 1. Gestational diabetes on adjusted analysis appeared to reduce the risk of antepartum stillbirth HR 0.

Adjusted risk factors stratified by parity are shown in Table 3. Multiparous women whose previous birth was by caesarean section had an increased risk of antepartum stillbirth that was close to but did not reach statistical significance HR 1. The dose response relationship with maternal age for increased antepartum stillbirth risk was seen for both nulliparous and multiparous women but with slightly larger effect sizes in the nulliparous women.


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  6. The increased risk seen overall for women residing in Sydney South Western Area Health Service is only significant for multiparous women which may reflect referral patterns into tertiary centres for known higher risk pregnancies. For women from Middle East and Africa there remains increased risk despite parity. The increased risk for women from Melanesia, Polynesia and Micronesia and Russia and the Baltic states was only seen for multiparous women and conversely only in nulliparous women from Southern Asia, and may be a reflection of the small numbers.

    Gestational age specific risk of antepartum stillbirth is shown in Figure 1. The risk of antepartum stillbirth per undelivered pregnancy rises from around 40 weeks for all women. Importantly this increased risk is apparent at earlier gestation by 1 — 2 weeks for women aged 40 and above.

    Absolute risks per undelivered pregnancies by parity for all age groups beyond 40 weeks of pregnancy are presented in Table 4. The overall absolute risk of stillbirth beyond 40 weeks gestation for women aged 40 and above was 2. The risk is highest for nulliparous women aged 40 and above with 1 in ongoing pregnancies resulting in an antepartum stillbirth. Risk of antepartum stillbirth by gestational age and maternal age group New South Wales Australia — Median gestational age at delivery.

    Ghaferi and colleagues [25] also showed that patients in small hospitals which had only slightly higher complications rates odds ratio 1. They suggested that large hospitals were more capable of effectively rescuing patients from complications compared to their low volume counterparts. The success of regional policy interventions and quality improvement programs varies, resulting in a different level of care quality across catchment areas.

    There are significant variations in post non-cardiac surgery mortality rates among 28 European nations [26] and in complication and mortality rates among U. Furthermore, a better understanding of the spatial distributions of FTR may facilitate the design and prioritisation of regional quality improvement interventions [26].

    The APDC includes information on patient demographics, medical conditions and procedures, hospital characteristics, and separations discharges, transfers and deaths from all public and private hospitals, as well as day procedure centres in NSW. Of all admissions at healthcare facilities in NSW between 1 st January to 31 st December , we included all 82 public acute hospitals in NSW 9,, admissions; Following methodology by AHRQ, we defined FTR as mortality among surgical patients who developed at least one of six serious treatable complications during hospitalisation; including acute renal failure, deep vein thrombosis or pulmonary embolism, pneumonia, sepsis, shock or cardiac arrest, gastrointestinal haemorrhage or acute ulcer [8].

    Applying AHRQ inclusion criteria [8] , patients who had elective surgery within two days of admission, aged between 18—90 years inclusive and who were transferred to an acute care facility were considered in the population study.

    We selected NSW Local Government Areas LGA; in total as the spatial units through the aggregation of patient postcode in total using appropriate concordance references [30]. Aggregated age and gender of patients and the distance between patient residential postcode and the treating hospital in each study year at LGA level were included in the adjustment.

    We did not include either of them in the adjustment given the recent reports that these indices may introduce misleading results possibly due to geographical variations and biases in the coding [34] — [36]. We applied an empirical Bayes index modification of Moran's I [37] to evaluate the global association. Local spatial association measures LISA were also used to identify clusters. A cluster of neighbouring LGAs with high or low FTR rates and significant local indicators was regarded as a hot or cold spot [38] , [39].

    We then utilised spatiotemporal modelling techniques to investigate spatial and temporal patterns of adjusted FTR rates across LGAs during the study period.


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    We derived areal adjusted relative risk compared to overall FTR rate using a geo-additive mixed effect model where a Poisson distribution underlies the observed FTR count, , for LGA, , at year, [40]. A Bayesian hierarchical model was defined to incorporate effects of covariates and spatial and temporal structure through random and fixed components. We followed the model specification proposed for spatiotemporal models in Gaussian Markov Random Field framework [41] , [42]. For spatial patterns, we used two latent random effects components: a spatially structured random effect, , which was modelled as a so-called intrinsic Conditionally Autoregressive iCAR ; and a spatially unstructured effect, , which was modelled as an independent normal distribution [41] , [43].

    The former accounts for any spatial autocorrelation, whilst the latter captures unexplained variation across LGAs and over dispersion. To model temporal patterns, we extended the parametric formulation proposed by Bernardinelli and colleagues [44].

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    In a semi-parametric approach, we included two time trends, linear and quadratic, as the fixed effect in the model a linear, , and a quadratic, , and then added a random effect term, , to account for any departure from the parametric fixed effects. We set time at the centre of study period To incorporate interaction between space LGAs and time linear and quadratic , two additional random effect terms, and , were incorporated into the model. These terms are analogous to the random slope formulation capturing model differential trends of the LGA, since they can be interpreted as the amount by which the time trends of i th LGA differ from the overall linear and quadratic time trends [44] , [45].

    Covariates were set as continues variables and assumed to have fixed effects. In prior specification, we let fixed effects, , , and , follow a zero-mean normal distribution with a very large variance of 10 3. The unstructured random spatial term , and all random effect terms, , and , were set as a zero-mean normal distribution with an unknown precision following a Gamma distribution with shape and scale parameters of and , respectively.

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    2006 new south whales australia public vital records 2006 new south whales australia public vital records
    2006 new south whales australia public vital records 2006 new south whales australia public vital records
    2006 new south whales australia public vital records 2006 new south whales australia public vital records
    2006 new south whales australia public vital records 2006 new south whales australia public vital records
    2006 new south whales australia public vital records 2006 new south whales australia public vital records
    2006 new south whales australia public vital records 2006 new south whales australia public vital records

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