scholarly journals Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
E. Amene ◽  
B. Horn ◽  
R. Pirie ◽  
R. Lake ◽  
D. Döpfer
Keyword(s):  
New Zealand ◽  
Model Based ◽  
Notification Data

scholarly journals Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand

2021 ◽  
Vol 18 (175) ◽  
pp. 20200964
Author(s):  
Jackie Benschop ◽  
Shahista Nisa ◽  
Simon E. F. Spencer
Keyword(s):  
New Zealand ◽  
Public Health Surveillance ◽  
Dairy Farm ◽  
Nucleic Acid Detection ◽  
Dairy Farmers ◽  
Occupational Groups ◽  
Specific Occupation ◽  
Health Surveillance Data ◽  
Notification Data ◽  
Important Evidence

Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as ‘dairy farm fever’ and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to predominate notifications. In recent years, diagnosis is increasingly made by nucleic acid detection which currently does not provide serovar information. Serovar information can assist in linking the recognized maintenance host, such as livestock and wildlife, to infecting serovars in human cases which can feed back into the design of intervention strategies. In this study, confirmed and probable leptospirosis notification data from 1 January 1999 to 31 December 2016 were used to build a model to impute the number of cases from different occupational groups based on serovar and month of occurrence. We imputed missing occupation and serovar data within a Bayesian framework assuming a Poisson process for the occurrence of notified cases. The dataset contained 1430 notified cases, of which 927 had a specific occupation (181 dairy farmers, 45 dry stock farmers, 454 meatworkers, 247 other) while the remaining 503 had non-specified occupations. Of the 1430 cases, 1036 had specified serovars (231 Ballum, 460 Hardjo, 249 Pomona, 96 Tarassovi) while the remaining 394 had an unknown serovar. Thus, 47% (674/1430) of observations had both a serovar and a specific occupation. The results show that although all occupations have some degree of under-reporting, dry stock farmers were most strongly affected and were inferred to contribute as many cases as dairy farmers to the burden of disease, despite dairy farmer being recorded much more frequently. Rather than discard records with some missingness, we have illustrated how mathematical modelling can be used to leverage information from these partially complete cases. Our finding provides important evidence for reassessing the current minimal use of animal vaccinations in dry stock. Improving the capture of specific farming type in case report forms is an important next step.

Family Group Conferences: Partnership in Practice

1996 ◽  
Vol 20 (1) ◽  
pp. 16-23 ◽  
Author(s):  
Murray Ryburn ◽  
Celia Atherton
Keyword(s):  
Social Work ◽  
New Zealand ◽  
Work Practice ◽  
Social Work Practice ◽  
Family Group ◽  
Family Group Conference ◽  
Model Based ◽  
The Family ◽  
The Uk

The quality of relationship between families and professionals is clearly crucial to the development of good social work practice, especially where the care and protection of children are concerned. After tracing the origins of the Family Group Conference in New Zealand, Murray Ryburn and Celia Atherton describe the procedure and explain how this model, based on a commitment to partnership, is being adapted and used in the UK.

Microbial biofacies in hot-spring sinters; a model based on Ohaaki Pool, North Island, New Zealand

1998 ◽  
Vol 68 (3) ◽  
pp. 413-434 ◽  
Author(s):  
B. Jones ◽  
R. W. Renaut ◽  
M. R. Rosen
Keyword(s):  
New Zealand ◽  
Hot Spring ◽  
Model Based

scholarly journals An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases

2005 ◽  
Vol 134 (4) ◽  
pp. 863-871 ◽  
Author(s):  
P. ROLFHAMRE ◽  
K. EKDAHL
Keyword(s):  
New Zealand ◽  
Statistical Models ◽  
Hepatitis A ◽  
Disease Outbreaks ◽  
Epidemiological Data ◽  
Average Sensitivity ◽  
England And Wales ◽  
Baseline Levels ◽  
Notification Data ◽  
The Impact

We evaluated three established statistical models for automated ‘early warnings’ of disease outbreaks; counted data Poisson CuSums (used in New Zealand), the England and Wales model (used in England and Wales) and SPOTv2 (used in Australia). In the evaluation we used national Swedish notification data from 1992 to 2003 on campylobacteriosis, hepatitis A and tularemia. The average sensitivity and positive predictive value for CuSums were 71 and 53%, for the England and Wales model 87 and 82% and for SPOTv2 95 and 49% respectively. The England and Wales model and the SPOTv2 model were superior to CuSums in our setting. Although, it was more difficult to rank the former two, we recommend the SPOTv2 model over the England and Wales model, mainly because of a better sensitivity. However, the impact of previous outbreaks on baseline levels was less in the England and Wales model. The CuSums model did not adjust for previous outbreaks.

Estimating rheumatic fever incidence in New Zealand using multiple data sources

2014 ◽  
Vol 143 (1) ◽  
pp. 167-177 ◽  
Author(s):  
J. OLIVER ◽  
N. PIERSE ◽  
M. G. BAKER
Keyword(s):  
New Zealand ◽  
Rheumatic Fever ◽  
Public Health Problem ◽  
High Risk Group ◽  
Surveillance Data ◽  
Data Sources ◽  
Important Public Health Problem ◽  
Multiple Data ◽  
Notification Data ◽  
And Control

SUMMARYRheumatic fever (RF) is an important public health problem in New Zealand (NZ). There are three sources of RF surveillance data, all with major limitations that prevent NZ generating accurate epidemiological information. We aimed to estimate the likely RF incidence using multiple surveillance data sources. National RF hospitalization and notification data were obtained, covering the periods 1988–2011 and 1997–2011, respectively. Data were also obtained from four regional registers: Wellington, Waikato, Hawke's Bay and Rotorua. Coded patient identifiers were used to calculate the proportion of individuals who could be matched between datasets. Capture–recapture analyses were used to calculate the likely number of true RF cases for the period 1997–2011. A range of scenarios were used to correct for likely dataset incompleteness. The estimated sensitivity of each data source was calculated. Patients who were male, Māori or Pacific, aged 5–15 years and met the Jones criteria, were most likely to be matched between national datasets. All registers appeared incomplete. An average of 113 new initial cases occurred annually. Sensitivity was estimated at 80% for the hospitalization dataset and 60% for the notification dataset. There is a clear need to develop a high-quality RF surveillance system, such as a national register. Such a system could link important data sources to provide effective, comprehensive national surveillance to support both strategy-focused and control-focused activities, helping reduce the incidence and impact of this disease. It is important to remind clinicians that RF cases do occur outside the well-characterized high-risk group.

Forecasting catastrophic stratovolcano collapse: A model based on Mount Taranaki, New Zealand

Geology ◽  
2012 ◽  
Vol 40 (11) ◽  
pp. 983-986 ◽  
Author(s):  
Anke V. Zernack ◽  
Shane J. Cronin ◽  
Mark S. Bebbington ◽  
Richard C. Price ◽  
Ian E.M. Smith ◽  
...  
Keyword(s):  
New Zealand ◽  
Model Based

scholarly journals A Consistent Geological-Seismological Model For Earthquake Occurrence in New Zealand

2021 ◽  
Author(s):  
◽  
Christian Stock
Keyword(s):  
New Zealand ◽  
Active Faults ◽  
Kernel Estimation ◽  
Fault Model ◽  
Strike Slip ◽  
Historical Earthquake ◽  
Earthquake Occurrence ◽  
Model Based ◽  
Adaptive Kernel ◽  
Dynamic Stochastic

<p>For the development of earthquake occurrence models, historical earthquake catalogues and compilations of mapped, active faults are often used. The goal of this study is to develop new methodologies for the generation of an earthquake occurrence model for New Zealand that is consistent with both data sets. For the construction of a seismological earthquake occurrence model based on the historical earthquake record, 'adaptive kernel estimation' has been used in this study. Based on this method a technique has been introduced to filter temporal sequences (e.g. aftershocks). Finally, a test has been developed for comparing different earthquake occurrence models. It has been found that the adaptive kernel estimation with temporal sequence filtering gives the best joint fit between the earthquake catalogue and the earthquake occurrence model, and between two earthquake occurrence models obtained from data from two independent time intervals. For the development of a geological earthquake occurrence model based on fault information, earthquake source relationships (i.e. rupture length versus rupture width scaling) have been revised. It has been found that large dip-slip and strike-slip earthquakes scale differently. Using these source relationships a dynamic stochastic fault model has been introduced. Whereas earthquake hazard studies often do not allow individual fault segments to produce compound ruptures, this model allows the linking of fault segments by chance. The moment release of simulated fault ruptures has been compared with the theoretical deformation along the plate boundary. When comparing the seismological and the geological earthquake occurrence model, it has been found that a 'good' occurrence model for large dip-slip earthquakes is given by the seismological occurrence model using the Gutenberg-Richter magnitude frequency distribution. In contrast, regions dominated by long strike-slip faults produce large earthquakes but not many small earthquakes and the occurrence of earthquakes on such faults should be inferred from the dynamic fault model.</p>

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