Analysis of Household Crowding
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- Bu səhifədəki naviqasiya:
- Income
- Housing tenure and sector of landlord
- Fuel use and heating
- Visitors to households
- Implications
- Conclusion
- References
EthnicityTable 4a shows that 40 percent of Pacific peoples of all ages are living in crowded households. For Māori, the figure is 20 percent; for Asians, it is 18 percent and for Europeans, it is 4 percent. For age groups 0–19, there is a slightly higher percentage of crowding across all ethnicities. The youngest age group (0–4 years) has the highest level of overcrowding for all three ethnicities of: Pacific peoples (45%), Māori (26%) and Asians (20%). Table 4a: Crowding by ethnicity
Household crowding by total responses of usual resident for households in private dwellings. IncomeIn Table 5a below, incomes are shown by quintile, being five equal-sized groups into which the population is divided according to the distribution of income values. For example, quintile 5 is made up of the 20 percent of people in the population with the highest incomes. There is a roughly linear relationship between crowding and income. The proportion of crowded houses increases as household income decreases. For the lowest income quintile (1), 15 percent of houses are crowded; for the highest quintile (5), just 2 percent of houses are crowded (Table 5a). Table 5a: Crowding by Jensen Equivalised Annual Household income quintile
Household crowding and JEAH income quintile for households in private dwellings. Table 5b: Crowding in low-income households, by ethnicity and DHB – percent crowded
There are strong associations between urban DHB crowding and low income for Pacific and Māori families, but the same association is not as clear for European and Asian families. For low income households, crowding is most significant in Counties Manukau, where the highest levels of crowding nationally are seen for Pacific (48%), Māori (35%) and European (10%), and the second highest level of crowding for Asian ethnicities (22%). For Pacific peoples, crowding is highest in Counties Manukau (48%), Auckland (48%), Waitemata (38%), Hutt Valley (36%), Capital & Coast (34%) and Hawke’s Bay (32%) For Māori people, crowding is highest in Counties Manukau (35%), Auckland (27%), Tairawhiti (25%) and Hawke’s Bay (24%). The rate of crowding for Māori is almost 10 percent higher in Counties Manukau than in any other DHB (Table 5b). Housing tenure and sector of landlordRented properties have the highest levels of crowding (18.6%). Properties that are owned (whether mortgage payments are made or not made) have considerably less crowding. For example, dwellings held in a family trust or owned ‘mortgage free’ have the lowest levels of crowding (3.5%), followed by dwellings that are owned and making mortgage payments (5.7%) (Table 6a). Table 6a: Crowding by tenure of household in New Zealand
Household crowding and tenure of household for usual residents in household, in private dwellings by DHB. In the two DHBs with the most crowded households, Counties Manukau and Auckland, the proportion of crowding is higher in all three household tenure categories than in all other DHBs. The proportion of crowding in rental accommodation in Counties Manukau (34%) and Auckland (30%) is significantly higher than the crowded rental accommodation of the next most crowded DHB region – Tairawhiti (23%) (Table 6b). Table 6b: Crowding in low-income households, by household tenure and DHB – percent crowded*
* Household crowding and tenure of household by JEAH income quintile (1+2 combined) by DHB. Nationally, the most crowded quintile (5) of area units (with between 13.4 and 58.2% crowding), have a much higher proportion of Housing New Zealand managed homes (Table 6c). Table 6c: Crowding by sector of landlord for households in rented private dwellings
Fuel use and heatingTable 7a shows the type of fuel used for heating by crowding status. Each column shows the total number of households using that fuel type and how many of those households are crowded. There is a range of fuel used for heating across both crowded and non-crowded households, with electricity being the most common type of heating used for both types of households. However, crowded households are more than three times more likely than non-crowded households to not have any type of heating for their home (9% compared with 3% respectively). Crowded households also use bottled gas more often than non-crowded households (19% compared with 16% respectively). Table 7a: Crowded households by heating type used by residents in New Zealand*
* Household crowding and fuel type – total responses for usual residents in household, in private dwellings. Total New Zealand row includes responses where crowding information was not available. Therefore the total responses in the table will be more than the total number of crowded and not crowded. In the Auckland region (Auckland, Counties Manukau and Waitemata DHB regions), 27,330 people in crowded households use no heating in their household. This is 16 percent of people in crowded households in the Counties Manukau DHB region, 15 percent in Auckland and 10 percent of people in crowded households in the Waitemata DHB region. There is high variance in the level of bottled gas use in crowded households, with the highest level of use being outside the main centres in the Bay of Plenty, Hawke’s Bay, Northland and Tairawhiti DHB regions (Table 7b). Table 7b: Crowding by heating type used by residents, by DHB region
* Includes all people who stated using more than one fuel type for heating were counted in each stated category. Therefore the total responses in the table will be more than the total number of people in households for occupied private dwellings. Visitors to householdsNight crowding for all households increases for all New Zealanders and by Euorpean, Maori, Asians ethnicities. However the increase is not significant for Maori. For Pacific peoples night crowding actually decreases, but this is not statistically significant (Table 8a). Table 8a: Census night DHB region, American Crowding Index: all households
Crowding increases for all ethnicities in low-income households when visitors to the household are included in the count but not significantly for Pacific People (Table 8b). For low-income European households, crowding was 0.2 percent higher when the count included visitors on census night; for Māori, it was 0.5 percent higher, for Pacific peoples, it was 0.7 percent higher and for the Asian ethnicity, it was 0.6 percent higher. Thus it can be seen that the increase is slightly more for Māori and Pacific peoples low-income families than for the other ethnicities. This result signals that, particularly for low-income families, visitors are a significant factor that has not been considered in previous census-based analysis of household crowding (Table 8b). Note: The American Crowding Index was used for measurement purposes for this table only because CNOS does not measure the impact of visitors. Table 8b: Census night DHB region, American Crowding Index: low-income households
ImplicationsThis report provides a picture of household crowding in New Zealand in 2013 in the context of age, ethnicity, income, housing tenure and landlord sector, fuel use for heating and visitors to the household. It is important that the characteristics and extent of household crowding in New Zealand are understood across the health and wider government sector because research has shown that crowding is linked to a number of poor health outcomes, including rheumatic fever (Baker et al 2013; Maani et al 2006). Although crowding analyses have been conducted previously, this report highlights what policy makers may see as key associations between crowding and the other factors assessed. This evidence reminds policy makers and health and social sector providers that communities experiencing high levels of crowding face a complex mix of challenges. Interventions are more likely to be successful in reducing crowding if such interventions take into account the varied and often complex circumstances of crowded households. Crowding is strongly associated with Māori, Pacific and, to a lesser extent, Asian ethnicities in New Zealand. However, Māori and Pacific peoples, and in particular Māori and Pacific children, are most likely to experience crowding along with poverty while also presenting for housing related illness and experiencing poorer health outcomes overall. The most prevalent levels of poverty and crowding are seen in the wider Auckland metropolitan area. Focusing on these areas within Auckland will enable service providers to reach communities where upwards of 40 percent of that community may be experiencing crowding. Interventions are likely to be more effective if agencies and providers enable communities to have meaningful input into service design and delivery of interventions to reduce crowding relevant to their community. Finally, crowding is related to a range of factors. While poverty is an important factor, this report shows that there are factors beyond socioeconomic deprivation that lead to crowding at all income levels. Future work could investigate the reasons for crowding and seek to answer the question, ‘Is crowding is associated with poorer outcomes over all income levels, or is it associated most closely with poorer outcomes for low-socioeconomic status households?’ ConclusionThis report highlights that, in New Zealand in 2013, there are still a significant number of New Zealanders living in crowded households. Further, there are inequities by age, ethnicity, income and spatial distribution (geographical area). Household crowding is relatively uncommon for most populations in New Zealand, with the exception of some sociodemographic groups, particularly Māori, Pacific peoples and children. These findings align with the suggestion that household crowding is making a considerable contribution to infectious disease burden in New Zealand, including rheumatic fever. Findings from this report support the continued need for interventions that are aimed at reducing household crowding for Pacific and Māori households in New Zealand, particularly those with children. Important limitations to consider include the lack of an internationally standardised method for measuring household crowding, as reflected in the range of definitions in use in this report. Similarly, there is no internationally agreed threshold for defining household crowding. This report excludes people living in non-private dwellings, such as boarding houses and night shelters because household and room data has not been collected for these dwellings. The census can only record some dimensions of household crowding, so it is important not to lose sight of the fact that there is potential for ‘functional crowding’ to be even greater than that estimated here (for example, in situations where families may sleep in a single room to keep warm over winter). This report can be used as evidence in the development of policy around household crowding. In addition, it sets a foundation for a range of possible further analysis or research. Some areas for further investigation include: children and household crowding, ethnic differences in household crowding based on income, and local-level detailed crowding analysis. ReferencesBaker M, Barnard GLT, Kvalsvig A, et al. 2012. Increasing incidence of serious infectious diseases and inequalities in New Zealand: a national epidemiological study. The Lancet 379(9821): 1112–19. Baker M, Goodyear R, Telfar Barnard L, et al. 2006. The Distribution of Household Crowding in New Zealand: An analysis based on 1991 to 2006 Census data. Wellington: He Kainga Oranga/ Housing and Health Research. Baker M, McDonald A, Zhang J, et al. 2013. Infectious Diseases Attributable to Household Crowding in New Zealand: A systematic review and burden of disease estimate. Wellington: He Kainga Oranga/Housing and Health Research. Cutts DB, Meyers AF, Black MM, et al. 2011. US housing insecurity and the health of very young children. American Journal of Public Health 101(8). Jaine R, Baker M, Venugopal K. 2011. Acute rheumatic fever associated with household crowding in a developed country. The Pediatric Infectious Disease Journal 30(4): 315–19. Maani SA, Vaithianathan R, Wolfe BL, et al. 2006. Inequality and Health: Is housing crowding the link? Wellington: Motu Economic and Public Policy Research. Thomson H, Thomas S, Sellstrom E, et al. 2013. Housing improvements for health and associated socio-economic outcomes. Cochrane Database Syst Rev 2. Yüklə 450,56 Kb. Dostları ilə paylaş:
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