These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

Techsalerator's Job Openings Data for New Zealand: A Comprehensive Resource for Employment Insights

Techsalerator's Job Openings Data for New Zealand is an essential resource for businesses, job seekers, and labor market analysts. This dataset provides a detailed overview of job openings across various industries in New Zealand, consolidating job-related information from multiple sources, such as company websites, job boards, and recruitment agencies.

Key Data Fields

• Job Posting Date: Tracks the date when a job is listed, allowing job seekers and employers to stay up-to-date with the latest job openings and employment trends.
• Job Title: Specifies the advertised position, helping users categorize and filter opportunities based on industry roles and career paths.
• Company Name: Lists the hiring company, assisting job seekers in targeting specific employers and enabling businesses to monitor competitors and market dynamics.
• Job Location: Provides the geographic location of the job within New Zealand, which helps job seekers find opportunities in specific regions, and employers assess regional talent availability and market conditions.
• Job Description: Details the responsibilities, qualifications, and expectations for the role, allowing candidates to assess whether they meet the requirements and recruiters to communicate clearly.

Top 5 Job Categories in New Zealand

1. Information Technology (IT): There is significant demand for software engineers, cybersecurity experts, and data analysts as New Zealand's tech sector continues to grow.
2. Healthcare: Jobs in nursing, general practice, medical technology, and healthcare administration are in high demand as the country addresses its healthcare needs.
3. Construction and Engineering: The industry seeks civil engineers, project managers, and construction workers due to ongoing infrastructure projects and urban development.
4. Education: Teachers, administrators, and educational consultants are needed as New Zealand focuses on improving its educational services.
5. Tourism and Hospitality: With a vibrant tourism sector, there is a steady demand for hotel managers, tour guides, chefs, and customer service professionals.

Top 5 Employers in New Zealand

1. Fonterra: The dairy cooperative regularly hires for roles in food production, logistics, IT, and research and development.
2. Air New Zealand: The national airline offers positions in aviation, customer service, engineering, and business management.
3. Auckland District Health Board: A major healthcare employer with opportunities for medical professionals, support staff, and healthcare administrators.
4. ANZ New Zealand: One of the country's largest banks, providing roles in finance, IT, marketing, and customer service.
5. University of Auckland: New Zealand’s leading university, frequently hiring for academic, administrative, and research positions.

Accessing Techsalerator’s Data

To access Techsalerator’s Job Openings Data for New Zealand, please contact info@techsalerator.com with your specific requirements. We offer customized quotes based on the data fields and records you need, with delivery available within 24 hours. Ongoing access options are also available for continued insights.

Included Data Fields

• Job Posting Date
• Job Title
• Company Name
• Job Location
• Job Description
• Application Deadline
• Job Type (Full-time, Part-time, Contract)
• Salary Range
• Required Qualifications
• Contact Information

Techsalerator’s dataset is an invaluable tool for those looking to stay informed about job openings and employment trends in New Zealand, enabling businesses, job seekers, and analysts to make data-driven decisions.

Introduction

This New Zealand English Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of English speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native New Zealand English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

This dataset is about countries per year in New Zealand. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, self-employed workers, and health expenditure per capita.

Hospitals in New Zealand increased to 31.95 per one million people in 2024 from 31.54 per one million people in 2023. This dataset includes a chart with historical data for New Zealand Hospitals.

Introduction

Introducing the New Zealand English Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of English language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in New Zealand English, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

The COVID-19 pandemic and the resulting Large-Scale Social Restrictions (PSBB) have significantly disrupted routine healthcare services, particularly in high TB burden countries. Despite initial expectations that the private health sector would lead in addressing TB, preliminary data suggests that the sector has shrunk or collapsed in many areas. Private facilities struggled to stay open during PSBB, and providers were reluctant to treat people with respiratory symptoms. Private healthcare costs have soared, especially for hospitalizations. Through this project, we were able to measure pre-treatment costs and factors associated with those costs from the perspective of patients during the COVID-19 pandemic in Bandung, Indonesia. It was found that the median total pre-treatment cost was $35.45 with the highest median cost experienced by participants from private hospitals. The rapid antigen and PCR for SARS-CoV-2 emerged as additional medical costs among 26% of participants recruited in private hospitals. Several factors are associated with higher pre-treatment costs including visiting more than 6 providers before diagnosis, presenting first at a private hospital and private practitioners, and being diagnosed in the private health sector. During the COVID-19 pandemic, people with TB faced significant out-of-pocket costs for diagnosis and treatment, highlighting the importance of early detection and identification in reducing pre-diagnostic TB costs.

New Zealand NZ: Current Health Expenditure: % of GDP data was reported at 9.340 % in 2015. This records a decrease from the previous number of 9.403 % for 2014. New Zealand NZ: Current Health Expenditure: % of GDP data is updated yearly, averaging 8.888 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 9.699 % in 2012 and a record low of 7.470 % in 2000. New Zealand NZ: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Health Statistics. Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Dataset for the maps accompanying the Housing in Aotearoa New Zealand: 2025 report. This dataset contains data for severe housing deprivation from the 2018 and 2023 Censuses.

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Data is available by health district.

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Severe housing deprivation has data for the census usually resident population from the 2018 and 2023 Censuses, including:

• estimated prevalence rate of severe housing deprivation (per 10,000 people)
• estimated rate for those; without shelter, in temporary accommodation, sharing someone else’s private dwelling, in uninhabitable housing, for whom it could not be determined whether they were severely housing deprived or not.

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Map shows the estimated prevalence rate of severe housing deprivation (per 10,000 people) for the census usually resident population for the 2023 Census.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Severe housing deprivation time series

The 2018 estimates of severe housing deprivation have been updated using the 2023 methodology for estimating severe housing deprivation. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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Severe housing deprivation

Figures in this map and geospatial file exclude Women’s refuge data, as well as estimates for children living in non-private dwellings. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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About the 2023 Census dataset

For information on the 2023 Census dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Census usually resident population count concept quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Quality of severe housing deprivation data

Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information on the data quality of this variable.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

BackgroundPhysical inactivity contributes substantively to disease burden, especially in highly car dependent countries such as New Zealand (NZ). We aimed to quantify the future health gain, health-sector cost-savings, and change in greenhouse gas emissions that could be achieved by switching short vehicle trips to walking and cycling in New Zealand.MethodsWe used unit-level survey data to estimate changes in physical activity, distance travelled by mode, and air pollution for: (a) switching car trips under 1km to walking and (b) switching car trips under 5km to a mix of walking and cycling. We modelled uptake levels of 25%, 50%, and 100%, and assumed changes in transport behaviour were permanent. We then used multi-state life table modelling to quantify health impacts as quality adjusted life years (QALYs) gained and changes in health system costs over the rest of the life course of the NZ population alive in 2011 (n = 4.4 million), with 3% discounting.FindingsThe modelled scenarios resulted in health gains between 1.61 (95% uncertainty interval (UI) 1.35 to 1.89) and 25.43 (UI 20.20 to 30.58) QALYs/1000 people, with total QALYs up to 112,020 (UI 88,969 to 134,725) over the remaining lifespan. Healthcare cost savings ranged between NZ$127million (UI $101m to 157m) and NZ$2.1billion (UI $1.6b to 2.6b). Greenhouse gas emissions were reduced by up to 194kgCO2e/year, though changes in emissions were not significant under the walking scenario.ConclusionsSubstantial health gains and healthcare cost savings could be achieved by switching short car trips to walking and cycling. Implementing infrastructural improvements and interventions to encourage walking and cycling is likely to be a cost-effective way to improve population health, and may also reduce greenhouse gas emissions.

Successfully implementing care technology to enhance people's health-related quality of life poses several challenges. Although many technological tools are available, we lack consensus on values and principles, regulatory systems, and quality labels. This article describes the FIDE process of the Belgian ‘Teckno 2030' project: Future-thinking Interdisciplinary workshops for the Development of Effectiveness principles, resulting in a framework of eight Caring Technology principles. These principles are built on three overarching values: autonomy, justice, and trust. The framework enables responsible health technology innovation by focusing on the needs of users and society, data security, equity, participatory governance, and quality control. A learning community was established to support the framework’s implementation in projects, organizations, and the broader innovation community. We also discuss the barriers, facilitators, and practical tools developed within this learning community. The FIDE process, caring technology principles, and learning community provide a case study for responsible innovation in care technology.

Context

With the arrival of the COVID19 virus in New Zealand, the ministry of health is tracking new cases and releasing daily updates on the situation on their webpage: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases and https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases/covid-19-current-cases-details. Much of the information given in these updates are not in a machine-friendly format. The objective of this dataset is to provide NZ Minstry of Health COVID19 data in easy-to-use format.

Content

All data in this dataset has been acquired from the New Zealand Minstry of Health's 'COVID19 current cases' webpage, located here: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases. The Ministry of Health updates their page daily, that will be the targeted update frequency for this dataset for the Daily Count of Cases dataset. The Case Details dataset which includes travel details on each case will be updated weekly.

Limitations of this dataset

The mission of this project is to reliably convey data that the Ministry of Health has reported in the most digestable format. Enrichment of data is currently out of scope.

Help improve this dataset

If you find any discrepancies between the Ministry of Health's data and this dataset, please provide your feedback as an issue on the git repo for this dataset: https://github.com/2kruman/COVID19-NZ-known-cases/issues.

This dataset was developed to understand the nutrient content of the commonly consumed foods in New Zealand.

• The linkages between two prominent New Zealand studies (or databases) were evaluated, in order to determine the matching between the foods in those studies.
• The first study was the 2008/09 New Zealand Adult Nutrition Survey (NZANS) for the total New Zealand population, which presents the commonly consumed foods (a total of 346) in New Zealand.
• The second study was the New Zealand FOODfiles™ 2018 Version 01, which is a database that provides nutrition information (both essential nutrients and essential amino acids) for 2,767 foods.
• Some of the foods in the 2008/09 NZANS are dishes (or menus); the nutrient content of each of those dishes was constructed based on the work by Drew et al. (2020).

References

1. Drew J, Cleghorn C, Macmillan A, Mizdrak A. 2020. Healthy and Climate-Friendly Eating Patterns in the New Zealand Context. Environ Health Perspect. 128(1): 017007.
2. Plant and Food Research, Ministry of Health. 2019. New Zealand Food Composition Database. The New Zealand Institute for Plant and Food Research Limited. [accessed 12 Feb 2020]. https://www.foodcomposition.co.nz/
3. University of Otago, Ministry of Health. 2011. A Focus on Nutrition: Key Findings of the 2008/09 New Zealand Adult Nutrition Survey. Wellington, New Zealand: Ministry of Health.

Last update: 12 September 2020

This dataset is about countries per year in New Zealand. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, health expenditure per capita, and birth rate.

Current scope of practice for optometrists in many countries include topical and oral medication with injectable and lasers being added more recently to scope in the United States (US), Canada, the United Kingdom (UK) and New Zealand (NZ). This expanded scope of optometric practice improves access to eyecare and is critical since an ageing population with a higher prevalence of vision disorders and higher healthcare costs looms. Expanded scope has been shown alongside strong safety records. This review paper aims to investigate the expansion of optometric scope of practice regarding lasers and injectables in the US, UK, Canada, Australia and NZ. The design and delivery of post-graduation educational programs, curriculum frameworks for advanced skills and the metrics of laser procedures performed by optometrists will be discussed. The State of Oklahoma in the US was first to authorise optometrists to use lasers and injectables in 1988. As of 2024, qualified optometrists in the UK, in twelve states in the US, and specialist optometrists in NZ perform laser procedures. However, lasers and injectables are not within the current scope of optometric practice in Australia and Canada. Training courses such as Northeastern State University Oklahoma College of Optometry Advanced Procedures Course and Laser Procedures Course have been successfully designed and implemented in the US to train graduate optometrists. The outcomes of over 146,403 laser procedures performed by optometrists across the US have shown only two negative outcomes, equating to 0.001%. These metrics outline the effectiveness of these procedures performed by optometrists and show strong support for future optometric scope expansion. Eye health professionals, relevant educational institutions, advocacy groups, and policymakers are called upon to work collaboratively to expand the optometric scope of practice globally.

This dataset includes information on mental health and addiction services (care) provided by secondary organisations funded by Te Whatu Ora (and prior to 1 July 2022, the Ministry of Health). Specifically, it covers demographic and geographic information, client referral pathways, the types of services provided, the outcome of the services and legal status and diagnosis information.

Cawthron Institute (Cawthron) has recently compiled a national dataset containing ecological estuary monitoring data (2001 to 2016) largely acquired from councils and unitary authorities (councils) around New Zealand. The dataset comprises fine-scale intertidal benthic ecological data collected using the Estuary Monitoring Protocol (EMP: Robertson et al. 2002), or similar, survey methodologies. This is in the form of macrofaunal abundance data and corresponding physico-chemical sediment data, as well as associated metadata. The dataset was compiled to facilitate national-scale research within the MBIE-funded Oranga Taiao, Oranga Tangata (OTOT) programme, led by Murray Patterson from Massey University. For further details on the dataset please refer to the National Estuary Dataset User Manual, which is included as a reference in the FigShare data repository. Use of the dataset is entirely at the risk of the recipient and we accept no responsibility for any inaccuracies that may be present.Subsets of this dataset have been used to 1) summarise benthic ecological health indicators from New Zealand estuaries, 2) test the suitability of nine biotic indices for assessing the health of New Zealand estuaries, and 3) develop a standardised indicator of the health of New Zealand estuaries in response to sedimentation and metal loading. These paper are also included as references in the FigShare Data Repository.

Context

In October 2021 the World Health Organization (WHO) published an article about how Mental Health services have been pushed to the limit during the COVID-19 pandemic while stating that the next pandemic will be on mental health encouraging governments to increase their expenditure on Mental Health.

While searching for Mental Health service usage and expenditure in New Zealand we found that the information is spread in several excel spreadsheets from 2002 to 2020 with different sources, formats, and accessibility.

We then proceeded to gather some of the information provided by the New Zealand Ministry of health into 3 datasets that summarise the usage of these services in the last 20 years.

Inspiration

The main inspiration for this dataset was to find a way of building a continuous pipeline for future reference of Mental Health Service usage in New Zealand.

Our first approach was to match Government Expenditure and usage of Mental Health Services in NZ over the last years but finding information about specific government expenditure is hard to come by, So we decided to focus mostly in creating a solid dataframe about mental health service usage over the years.

Data Sources

Ministry of Health New Zealand

This data source was chosen for its ease of access and ability to web scrape.

Datasets were available from three sources: 1) Datasets from 2002 to 2008 2) Dataset of 2010 3) Dataset from 2011 to 2020

3 different crawlers were developed in order to maintain consistency over sources. Datasets from 2011 onwards are displayed in the ministry of health new Aggregated Data Site..

Datasets from 2002 were gathered from legacy sources on the list of reports by the ministry of health

The data provided in this data sets can be classified into 3 groups:

1) NZ Mental Health services usage by gender, age and ethnicity. 2) NZ Mental Health service usage by DHB's (District Health boards) 3) NZ Metal Health service usage by Service provided.

"Data is sourced from the Programme for the Integration of Mental Health Data (PRIMHD). PRIMHD contains Ministry of Health funded mental health and addiction service activity and outcomes data. The data is collected from district health boards (DHBs) and non-governmental organisations (NGOs).

PRIMHD data is used to report on what services are being provided, who is providing the services, and what outcomes are being achieved for health consumers across New Zealand's mental health sector. These reports enable better quality service planning and decision making by mental health and addiction service providers, at local, regional and national levels."

We have combined the data in the excel files provided by the MOH into a single data frame.

Intention of usage.

The idea behind the project is to have an incremental dataset for past and future reference, allow ease of access to timeseries information and better visibility.

Problems while gathering data.

We will divide the problems while gathering information into 2 categories: Downloading and Wrangling.

Downloading

The ministry of health releases an anual report on Mental Health since 2002, this reports are uploaded to the stats page of the Ministry of Health's website.

Reports from 2002 to 2007 have an aggregated site where they can be downlaoded programmatically. Reports from 2008 and 2010 have their individual site and had to be added manually to the download process. Reports from 2011 onwards have their own Mental Health page where they get uploaded every year. This website allows us to make incremental updates to the current dataset.

This 3 types of published papers required individual processes to download programmatically, 2 of them were scrapped from lists, and 1 of them manually added to the dataset.

While developing the download process for reports from 2011 we also find that some links were broken or required manual intervention, this had to be solved with exceptions for different years.

We developed parsers for this matter and expect changes in the future that can be solved by adding simple exceptions to new years given that they change, which at this stage is uncertain.

For the scraping part of the project we used R's rvest library.

Wrangling

The reports published by the Ministry of Health are given in excel format. R's tidiyverse and readxl libraries were used.

These reports are given in multi sheet excel files that have changed considerably over the years and had to be solved with individual parsers.

For this we...

This dataset is about countries per year in New Zealand. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, health expenditure per capita, and male population.