Population Health Data and Demographics in New Zealand

Learning Outcomes

  • Describe the key sources of population (health) data and their strengths/weaknesses.

  • Create population pyramids and interpret dependency ratios.

  • Understand how to calculate demographic measures and their applications.

  • Describe the effects of demographic changes on New Zealand's population.

Why We Need Population Data

  • Measuring Trends:

    • Births: Tracking birth rates over time.

    • Mortality: Analyzing death rates, including all-cause and cause-specific mortality.

    • Morbidity: Evaluating health conditions, both general and specific.

    • Migration: Understanding both immigration and emigration trends.

    • Social Indicators:

    • Unemployment rates, benefits, and pensions.

    • Crime statistics, covering broad and detailed classes of offences.

    • Health service utilization to determine service needs.

    • Political data like voter turnout and educational pathways.

    • Designing electoral boundaries and resource allocation.

Demographic Terminology

  • Population Attributes:

    • Population can be represented by age, sex (male/female), and other variables.

    • Population Structure: Defined by age and sex.

    • Population Composition: Defined by a wider range of attributes.

Population Pyramids

  • Construction:

    • X-axis: Males on left, females on right.

    • Y-axis: Age, represented as single years or in 5-year bands, from youngest (bottom) to oldest (top).

    • Bars: Represent either count or percentage of each age-sex group.

Key Data Sources for Epidemiology
Census

  • Role: Population data collection through ‘enumeration officers’.

  • Method: Enumerators contact households to deliver and collect census forms.

  • Meshblocks: The area divided into manageable segments (average 100 people).

  • Recent Change: Transition to 'online first' approach in 2018.

Estimated Resident Populations (ERP)

  • Estimates individuals who usually live in New Zealand, factoring in births, deaths, and long-term migration.

Health Service Utilisation (HSU)

  • Reports publically funded health information including hospitalizations and medications.

Integrated Data Infrastructure (IDI)

  • A de-identified data repository linking government service data, used for population-based studies.

Administrative Population Census (APC)

  • Uses administrative data to estimate NZ population structure (age, sex, etc.) since 2006.

Vital Events

  • Maintained by the Department of Internal Affairs, covering births, deaths, and marriages.

Data Considerations

  • Ethics and Privacy: Ensuring confidentiality and addressing the purpose of data use versus analysis.

  • Population vs Samples: Are collected samples representative of the entire population?

  • Objective vs Subjective Measures: Assessing the health status of populations.

Events Determine Population Structure

  • Age-sex Structure Influencers:

    • Birth and mortality patterns, immigration/migration trends.

    • Recent examples include impacts from the Christchurch earthquakes and COVID-19.

Migration Patterns

  • External Migration: Defined as immigration (arrivals) and emigration (departures).

  • Net Migration: Calculated as arrivals minus departures.

  • Internal Migration: Influences regional populations based on movement patterns (e.g., within Wellington).

Dependency Ratios

  • Calculations:

    • Child Dependency Ratio: rac{0-14 ext{ years}}{ ext{working age}} imes 100

    • Elderly Dependency Ratio: rac{ ext{Elderly} ext{ (≥65 years)}}{ ext{working age}} imes 100

    • Total Dependency Ratio: rac{ ext{Youth + Elderly}}{ ext{working age}} imes 100

Ethnic Composition of NZ

  • Variability based on data sources (Census, HSU, IDI).

  • Use of prioritzed and total response coding for ethnicity representation.

Strengths and Weaknesses of Ethnicity Data Outputs

  • Prioritized Output: Simplifies data representation but may bias statistics by over-representing some groups.

  • Total Response Output: Can represent complex identities but complicates data interpretation and funding allocation.

Conclusions

  • The quality of data is crucial in epidemiology; accurate definitions affect analysis and resource allocation.

  • Understanding demographic measures (fertility, mortality, migration) reveals insights into population health.

  • New Zealand’s ageing population presents challenges for health services and workforce dynamics as dependency ratios shift by 2026.