Injuries in Homes with Certain Built Forms
Ronan A. Lyons, MD, MPhil, BMBCh, Robert G. Newcombe, PhD, Sarah J. Jones, PhD, Joanne Patterson, BSc, Stephen R. Palmer, MA, Philip Jones, PhD
Background: Rates of injuries may occur more frequently in different types of homes.
Methods: Retrospective population-based cohort study utilizing three linked databases: a population register, an architectural assessment of homes in the area, and an emergency department-based injury surveillance system.
Results: Over 58,000 homes were classified into 94 different types according to age, size, and built form. Among the 112,248 inhabitants, there were 18,044 emergency department atten-dances for treatment of an injury suffered in the home. Adjusted odds ratio of injuries for residents of purpose-built apartments was substantially elevated for all injuries (2.07; 95% confidence interval [CI]⫽1.87–2.30) and poisoning episodes (5.6; 95% CI⫽3.8 – 8.3). Conclusions: Residents of apartment buildings have substantially higher injury rates. Additional research
is required to investigate the contribution of environmental hazards and behavioral factors underlying these high rates.
(Am J Prev Med 2006;30(6):513–520) © 2006 American Journal of Preventive Medicine
Introduction
T
he influence of the built environment on health is receiving renewed attention, particularly in relation to urban-renewal programs and devel-opment of sustainable communities.1One major theme is the relationship between the built environment and injuries, although research has focused mainly on traffic-related injuries rather than injuries occurring in the home. In the United States, unintentional injuries in the home account for some 18,000 deaths per annum, and 12 million injuries require medical atten-tion.2,3The societal cost of unintentional home injuries occurring in the United States in 1998 has been esti-mated at $217 billion.4Children are particularly vulner-able to home injuries. Approximately 55% of uninten-tional injury-related deaths and 39% of emergency department (ED) visits in children and young people in the United States result from injuries that occur in the home.5,6 In the United Kingdom, the British Medical Association’s Board of Science and Education report that more unintentional injury deaths occur in the home than on the road, and that every year more than 1 million UK children are injured at home.7 Despite this, there has been very little work looking at whether particular types of homes are more hazardous. In theUnited Kingdom, the newly adopted Housing Health and Safety Rating System (HHSRS) suggests that pre-1919 dwellings are substantially more hazardous than post-1980 dwellings,8,9 but how this conclusion was reached and the extent to which adjustment was made for potential confounders is not clear. A multilevel study in the United States reported that pediatric injuries are more common in housing units built before 1950.10
The objective of this population-based study was to determine whether injuries occur more commonly in different types of homes. The magnitude of differential injury rates would be useful in assessing the feasibility and desirability of targeted or universal approaches to injury prevention in the home.
Methods
This study was carried out within a long-term partnership between academia and local government to understand the relationship between the social and built environments and health in order to develop interventions to improve health.11 The research was carried out in the Neath-Port Talbot County area in South Wales in the United Kingdom, in which injury-event data from an injury surveillance system were linked with a register of property types and with a population register.
Research exploited a Geographic Information Systems (GIS)-based energy-use prediction model of the entire build-ing stock of the local community developed by the Welsh School of Architecture. This Energy and Environmental Prediction project involved an external assessment of all individual homes between May 1999 and January 2002, and included details on house size, type, and age of property.12
From the School of Medicine, University of Wales Swansea (Lyons), Swansea, Wales; Department of Epidemiology, Statistics and Public Health, (Newcombe, SJ Jones, Palmer); and the Welsh School of Architecture, Cardiff University (Patterson, P Jones), Cardiff, Wales
Address correspondence and reprint requests to: Ronan A. Lyons, MD, The School of Medicine, University of Wales Swansea, Grove Building, Singleton Park, Swansea SA2 8PP. E-mail: r.a.lyons@ swansea.ac.uk.
Domestic properties were classified into four categories— based on floor area in square meters (square feet): small⬍47 (⬍1660); medium 47 to 55 (1660 –1942); large 55 to 68 (1942–2402); and very large ⬎68(⬎2402); five age groups: pre-1919, 1919 to 1945, 1946 to 1964, 1965 to 1978, and 1980 to 2002; and five types: detached houses, semi-detached, houses converted to apartments, apartment buildings, and terraced or row houses— giving a total of 100 possible groups. Ninety-four combinations of the three housing-type variables were found in the study area. Figures 1 through 5 show examples of the most frequent property types. Table 1shows the distribution of property type in the study area and a comparison with the 2001 English House Condition survey.13 Analysis was carried out on groups of individual properties at the individual-property level. Analysis at postcode (ZIP code) level, where each postcode contains an average of 14 proper-ties, was considered but was not possible because very few postcodes (13%) were composed of a single property type.
Data on injuries treated at hospital EDs were extracted for the period 1999 to 2000 from the All Wales Injury Surveil-lance System that collates individual-level data from sur-rounding EDs and is described in detail elsewhere.14Briefly,
the data comprise the patient’s address, age, gender, date of occurrence, type and anatomical site of injury (up to three diagnoses and three sites can be coded), and includes a code indicating whether the injury occurred at home or elsewhere. ED administrative staff inquiries include whether the injury occurred at home (include gardens), at work, on a public road, or in an educational establishment. In common with ED injury surveillance systems in the United States, approxi-mately one quarter of records have a missing categorical code on injury location.15The final home code was based on the categorical code and structured analysis of narrative in two other fields, as described in a previous publication.16
The National Health Service Administrative Register (NHSAR), a list of all people registered with the no-fee primary care health service in Wales, was used to derive
Figure 1. Example of a detached house.
Figure 2. Example of terraced houses.
Figure 3. Example of converted apartments.
denominator data for each property type. Data on this system are highly confidential; denominator population profiles were obtained by providing a list of all properties in the 94 different groups to the NHSAR staff, who then matched the data sets to obtain the number of people in each of the property groups, subdivided by age and gender. Data were not released for individual postcodes (10 –14 houses or prop-erties) because the numbers in each 5-year age/gender group, or even in total, could have been very small and so become a threat to data confidentiality.
In analyzing data on injury attendance at hospitals or EDs, the potential biases associated with area deprivation and ease of access were accounted for. Deprivation was measured by the Townsend Index of Multiple Deprivation17,18; this is a neighborhood score derived from 10 years of census data based on homeownership, employment, overcrowding, and access to a car.18 Ease of access to free healthcare in the United Kingdom has a profound effect on attendances at EDs; previous work had shown a twofold difference in child-hood injury attendances at EDs over a 10-mile distance.19 Access was measured by linear distance from postcode cen-troid to the nearest ED.
Combining these data sets to study the relationship be-tween injury and property types was complex. For instance, it was not possible to construct a comprehensively linked data set at the individual level because, although it was possible to assign an exact Townsend deprivation score and distance to hospital based on address of each injured individual using
data for the surveillance system, confidentiality of the NHSAR data meant that this was not possible to do the same for uninjured individuals. Actual deprivation and distance scores were used for the injured, but randomly sampled Townsend and distance scores were used for noninjury properties; imputed values were used to assign these variables to unin-jured individuals. Thus, we used a combination of determin-istic and probabildetermin-istic matching of individuals across data sets.20 The accuracy of this approach was determined by
comparing the actual and expected Townsend Scores in the 14,081 individuals with an injury. The mean actual Townsend Score was 0.85 and the imputed score 0.86 in this group. The results were replicated after a second randomization and were almost identical. The linkage process used is described in greater detail elsewhere.21
Logistic regression analyses were carried out, both univar-iate and multivarunivar-iate (without variable selection techniques), and odds ratios (ORs) with 95% confidence intervals (CIs) were computed. Townsend score was entered as a linear covariate; all other variables appeared in categorical form. Approximate quintile groups were used to represent proxim-ity to hospital. Age was recoded into 21 groups, under 1 year, 1 to 4 year, and 5-year age groups thereafter, subjects aged 95 and over constituting a final group. A 42-category variable was used to represent age and gender jointly; apart from this, no interaction terms were included in the model. The most frequent property type, semi-detached houses, was chosen as the reference population.
Results
A total of 62,943 properties were identified from a database held by the local authority, of which 60,171 (95.6%) could be mapped on to a GIS using Ordnance Survey software (Southhampton, UK). In this group, 58,041 properties were classified as domestic properties and 55,148 (87.6% of total) were visible from the road, surveyed, and assigned built-form codes. Nursing and care homes were subsequently removed by cross-checking with local registers, leaving 54,913 individual homes (of which 54,081 had complete data) that could be linked to the injuries data. Assuming the distribu-Figure 5. Example of a semi-detached house.
Table 1. Number of housing types by period and comparison of distribution (percentages) with the 2001 English House Condition Survey
Period built
Area Pre-1919 1919–1944 1945–1964 1965–1980 1981ⴙ
Housing type n % n % n % n % n %
Detached house Study 1818 3.3 698 1.3 918 1.7 2881 5.2 1494 2.7
England 2.8 3.1 4.7 6.8 7.7
Terraced house Study 12,641 23.0 821 1.5 1483 2.7 1458 2.7 661 1.2
England 11.5 4.5 3.9 4.8 3.6
Converted apartments Study 262 0.5 17 ⬍0.1 10 ⬍0.1 11 ⬍0.1 19 ⬍0.1
England 2.7 0.4 0.1 ⬍0.1 ⬍0.1
Apartment building Study 35 ⬍0.1 6 ⬍0.1 1425 2.6 1454 2.6 844 1.5
England 0.6 1.3 3.9 5.3 4.4
Semi-detached house Study 4511 8.2 5478 10.0 10937 19.9 3898 7.1 1133 2.1
tion of domestic properties and nursing homes in those properties that could not be mapped or were not visible from the road was similar to those that were visible, then the 54,913 individual homes represent 91% of the estimated domestic properties in the study area.
Over a 2-year period, from a population of 112,248, there were 18,044 attendances at hospital for treatment of an injury occurring in the home. In total, 16,358 (90.6%) were matched to the GIS database. A small number of ED records had missing items. A total of 14,081 attendance records were identified as relating to the individual’s first attendance within the study period and were linkable. This identified the 14,081 individu-als who attended the ED with one or more home injuries within the period. These injured individuals were then compared with the remaining 98,167 unin-jured individuals in the population.
Table 2 shows the number of people resident in the five property types, the number of first-time injuries, and the injury proportion by broad age and gender groups. Age, gender, size of property, age of property, type of property, distance from hospital, and Townsend score were all independently related to injury rate (Table 3). Adjusted ORs for apartment buildings was 2.1 (range 1.8 –2.3) and was also slightly elevated for terraced houses (OR⫽1.1). Older properties were not associated with an increased risk. Property size (Table 3) was only weakly associated with increased risk.
Further analyses were carried out on injuries due to burns and scalds and from poisoning. There were 361 burns and scalds (Table 4) and 481 poisoning (Table 5) injuries among the 14,081 first-time injuries. Analyses of these injury types as proportions of all injured led to differential results, with residents in apartment build-ings having a twofold increase in risk from burns and scalds and a sixfold increase in poisonings. Analyses of second attendances (hence multiple attenders) also gave broadly similar patterns.
Discussion
Given the enormous scale of mortality, morbidity, health service utilization, and societal costs associ-ated with injuries in the home, prevention of such injuries should be a key public health and societal goal.15In order to target injury prevention initiatives, it is important to know which types of homes contain the most dangerous hazards. Residential injury is likely a result of complex interactions between the housing type and individual motor skills behavior and susceptibility.
A high injury rate in any particular type of property could indicate the presence of more physical hazards in that property type, or that people with particular types of behavior or susceptibility to injury are predomi-nantly housed in those property types, or interaction
between these extrinsic and intrinsic risk factors. Re- Table
2. Number and proportion of residents who ever presented at a hospital emergency department during the 2-year study period, by gender and age group, by housing type First injuries Gender/Age (group) Detached house Terraced house Converted apartment Apartment building Semi-detached house Total Male/0–19 260/1567 (16.6) 939/4647 (20.2) 5/34 (14.7) 51/138 (37.0) 1302/7303 (17.8) 2557/13,689 (18.7) Female/0–19 222/1570 (14.1) 814/4297 (18.9) 4/46 (8.7) 58/161 (36.0) 1174/6894 (17.0) 2272/12,968 (17.5) Male/20–64 412/4849 (8.5) 1129/10635 (10.6) 12/92 (13.0) 170/747 (22.8) 1737/16474 (10.5) 3460/32,797 (10.5) Female/20–64 439/4773 (9.2) 1183/10397 (11.4) 10/77 (13.0) 159/595 (26.6) 1880/16573 (11.3) 3671/32,418 (11.3) Male/65 ⫹ 103/1413 (7.3) 146/2436 (6.0) 1/16 (6.3) 42/367 (11.4) 366/4559 (8.0) 658/8791 (7.5) Female/65 ⫹ 170/1705 (10.0) 441/3379 (13.1) 3/15 (20.0) 128/684 (18.7) 721/5802 (12.4) 1463/11,585 (12.6) Total 1606/15,877 (10.1) 4652/35,791 (13.0) 35/280 (12.5) 608/2695 (22.6) 7180/57,605 (12.5) 14,081/112,248 (12.5) Note: Values in parentheses are percentages.
cent publication in home-hazard assessments in Austra-lia and the United States have identified the surprising frequency of many hazards as well as providing guid-ance on effective interventions.15,22
The relationship between built form and injury risk has not previously been studied on this population scale using architecturally defined built-form features. Virtually all the houses and their occupants in an entire community were linked to an injury surveillance system measuring the occurrence of home injuries severe enough to require treatment. The results show that injury incidence is related to property type, with partic-ularly high levels in apartment buildings. However, before accepting these results at face value, it is impor-tant to consider any potential biases that might affect the results. Even though healthcare is free at the point of contact in the UK National Health Service, socioeco-nomic status and physical access to treatment facilities are two potentially major sources of bias. Socioeco-nomic status is related to injury occurrence, but the strength of the relationship differs by type of injury, with much stronger relationships for pedestrian
inju-ries, burns, and poisoning than for falls.17,23 The cur-rent study is compatible with this body of work, showing higher odds ratios for a change in Townsend scores for poisoning, burns, and scalds than for or all injuries. However, the Townsend score is an area-based score, and there remains the possibility that there is some residual confounding by socioeconomic status of individuals.
Missing categorical codes on injury location is a common problem in ED surveillance systems affecting around one quarter of records in this and other stud-ies.15 Both categorical codes and narrative data from two fields were used to identify home injuries. This method identified 42% of all first-injury attendances at EDs among the residents of the study properties where the injuries occurred. It is likely that some home injuries were missed in records that were missing both categorical and narrative data, but the magnitude of this bias is unknown. In a previous study, the narrative text used to identify home injuries when the categorical field was missing had a 66% sensitivity and 92% sensitivity.16
Table 3. Unadjusted and adjusted odds ratios (95% confidence intervals) for first injury related to housing type, deprivation, and access factors
All first injuries (14,081 subjects of 112,248)
Factor Univariate model Multivariate model
Gender and age
2(41 df) 3384 (p⬍0.001) 3333 (p⬍0.001) Housing type Detached house 0.79 (0.75–0.84) 0.89 (0.89–0.95) Terraced house 1.50 (1.01–1.09) 1.11 (1.06–1.17) Converted apartments 1.00 (0.70–1.43) 1.11 (0.77–1.59) Apartment building 2.05 (1.86–2.25) 2.07 (1.87–2.30) Semi-detached house 1.00 1.00 2(4 df) 327.5 (p⬍0.001) 254.5 (p⬍0.001) Distance to ED (km) ⱕ4.26 1.43 (1.35–1.51) 1.47 (1.38–1.56) 4.27–5.57 1.29 (1.21–1.37) 1.41 (1.32–1.52) 5.58–8.69 1.38 (1.31–1.47) 1.36 (1.28–1.44) 8.70–13.25 1.17 (1.10–1.24) 1.24 (1.17–1.325) ⱖ13.26 1.00 1.00 2(4 df) 194.8 (p⬍0.001) 179.9 (p⬍.001) Period built Pre-1919 0.82 (0.76–0.89) 0.88 (0.81–0.96) 1919–1945 0.81 (0.74–0.88) 0.88 (0.80–0.97) 1945–1965 1.02 (0.95–1.10) 1.08 (0.99–1.18) 1965–1980 0.88 (0.81–0.96) 0.99 (0.91–1.08) 1980–2002 1.00 1.00 2(4 df) 121.4 (p⬍0.001) 67.8 (p⬍0.001) Townsend score 1.02 (1.014–1.03) 1.02 (1.01–1.02) 2(1 df) 36.8 (p⬍0.001) 15.4 (p⬍0.001) Floor area Small 1.18 (1.12–1.24) 1.04 (0.98–1.11) Medium 1.13 (1.07–1.19) 1.03 (0.97–1.10) Large 1.00 (0.95–1.05) 0.95 (0.90–1.01) Very large 1.00 1.00 2(3 df) 67.7 (p⬍0.001) 12.0 (p⫽0.007)
Results of this study also demonstrate the importance of proximity to healthcare, even in a country where that healthcare is free at the point of contact. By using linear distance as the metric of access, the effect of access is demonstrable in all the analyses with excess attendance rates of 43% to 63% for those residents within 4.26 km, compared with those living 13.26 km and farther away. There is a small possibility that some people in one or two electoral tracts in the most distant group might attend a minor casualty unit that does not supply data to the injury surveillance system. However, this would not materially affect the results, and there is evidence of distance decay effects within the remaining four categories of distance. This finding is in line with previous research on distance and access, and it under-lines the importance of its inclusion in the model.19
The odds ratio for injuries in apartment buildings is very similar for burns and scalds (OR⫽1.94) and all injuries (OR⫽2.07), whereas the ratio for poisonings is substantially elevated at 5.6. Additional research is required to determine whether intrinsic factors, such as frailty or behavioral issues, or extrinsic environmental
factors, such as housing hazards, are the underlying cause of the high injury rate in apartment buildings, and to a lesser extent in terraced housing.
The results of this study do not provide evidence that older homes are inherently more hazardous. This dif-fers radically from the recently published HHSRS in 2001, which suggested older housing stock is more hazardous.8,9 The HHSRS is a very detailed study of housing hazards using a large number of databases and covering many health aspects, including injury data from the Home Accident Surveillance System and postcode-level data on housing type and population to estimate the risk of injuries in different housing types. The HHSRS study involved the aggregation of data across groups of adjacent housing, estimating denomi-nators; it could not adjust for distance to hospital. These methodologic differences could explain why the current findings differ from those of the HHSRS. Despite the advantages of still greater size, analysis at postcode level within the HHSRS may have introduced considerable misclassification biases. From the current study, it is clear that housing type is not homogenous at Table 4. Unadjusted and adjusted odds ratios (95% confidence intervals) for first “burn and scald”–related injuries and housing type, deprivation, and access factors
Burn and scald injuries (361 subjects of 112,248)
Factor Univariate model Multivariate model
Gender and age
2(41 df) 471.0 (p⬍0.001) 465.3 (p⬍0.001) Housing type Detached house 0.94 (0.6744–1.31) 1.13 (0.76–1.67) Terraced house 1.27 (1.01–1.59) 1.28 (0.97–1.69) Converted apartments (0) (0) Apartment building 1.63 (0.93–2.88) 1.94 (1.05–3.60) Semi-detached house 1.00 1.00 2(4 df) 7.77 (p⫽0.10) 6.78 (p⫽0.15) Distance to ED (km) ⱕ4.26 1.55 (1.12–2.14) 1.55 (1.11–2.18) 4.27–5.57 1.12 (0.77–1.62) 1.21 (0.80–1.84) 5.58–8.69 1.22 (0.87–1.72) 1.22 (0.85–1.74) 8.70–13.25 1.18 (0.83–1.67) 1.28 (0.89–1.86) ⱖ13.26 1.00 1.00 2(4 df) 8.48 (p⫽0.08) 7.15 (p⫽0.13) Period built Pre-1919 0.82 (0.53–1.28) 0.85 (0.53–1.38) 1919–1945 0.77 (0.47–1.26) 0.89 (0.53–1.50) 1945–1965 0.88 (0.56–1.37) 1.02 (0.63–1.65) 1965–1980 0.92 (0.58–1.47) 1.18 (0.73–1.90) 1980–2002 1.00 1.00 2(4 df) 1.73 (p⫽0.79) 3.77 (p⫽0.44) Townsend score 1.04 (1.00–1.08) 1.04 (0.99–1.09) 2(1 df) 3.83 (p⫽0.050) 2.75 (p⫽0.10) Floor area Small 1.267 (0.94–1.70) 1.09 (0.76–1.56) Medium 1.13 (0.82–1.54) 1.03 (0.72–1.47) Large 1.17 (0.86–1.58) 1.13 (0.81–1.57) Very large 1.00 1.00 2(3 df) 2.48 (p⫽0.48) 0.71 (p⫽0.87)
postcode level, and treating postcodes as such will introduce biases of unknown magnitude and direction. Also, the relatively strong relationship between ED attendance and distance from hospital could introduce bias. In many areas, hospitals are still located in older areas of towns and cities, adjacent to older properties. In such a scenario, a higher rate of attendance of people from older properties would be expected. Ad-justment was made for this bias in these analyses. However, these data cannot distinguish specific haz-ards, such as between falls on stairs, on the level, or associated with baths, and cannot provide estimates of these particular hazards to compare directly with the HHSRS.
A multilevel study of social disparities in housing and related pediatric injury in Illinois reported higher injury rates in homes built before 1950.10This study was able to adjust for gender at the individual level and several factors at the ZIP code level (percentage owner-occupied housing, percentage housing built before 1950, concentrated poverty, and concentration of racial minorities). The ZIP codes contained an average of 10
properties. The odds ratios for injuries in the pre-1950 housing were elevated by around 10%.
Distribution of property types in the study area differs somewhat from that in England (Table 1), with older terraced housing, post-World War II semi-detached homes, and fewer high-rise apartment build-ings.13Apartment buildings in this study were generally in blocks two to three stories high. There was only one large block of apartments with more than five stories. Differences in built environments between and within apartment complexes may increase or decrease injury risk (e.g., access to and utilization of stairways com-pared with elevators). The 2003 American Housing Survey for the United States reports that 64% of homes are detached, 5.9% are town or row houses, 6.5% mobile homes or trailers, and 23.6% apartment build-ings.24 Distribution of housing types across countries similar in cultural and socioeconomic factors can still vary widely and act to increase or decrease injury risk. Replication of the study or adoption of the method-ology in different settings would provide welcome Table 5. Unadjusted and adjusted odds ratios (95% confidence intervals) for first poisoning-related injuries and housing type, deprivation, and access factors
Poisonings (481 subjects of 112,248)
Factor Univariate model Multivariate model
Gender and age
2(41 df) 304.3 (p⬍0.001) 304.9 (p⬍0.001) Housing type Detached house 0.73 (0.53–1.01) 1.05 (0.72–1.54) Terraced house 1.29 (1.06–1.58) 1.41 (1.11–1.80) Converted apartments 1.89 (0.47–7.66) 2.31 (0.56–9.50) Apartment building 4.17 (2.99–5.81) 5.61 (3.79–8.32) Semi-detached house 1.00 1.00 2(4 df) 83.3 (p⬍0.001) 84.9 (p⬍0.001) Distance to ED (km) ⱕ4.26 1.51 (1.12–2.04) 1.63 (1.20–2.22) 4.27–5.57 1.38 (0.997–1.90) 1.80 (1.25–2.58) 5.58–8.69 1.67 (1.24–2.24) 1.61 (1.19–2.20) 8.70–13.25 1.23 (0.89–1.69) 1.50 (1.07–2.09) ⱖ13.26 1.00 1.00 2(4 df) 13.8 (p⫽0.01) 13.3 (p⫽0.01) Period built Pre-1919 0.74 (0.51–1.07) 0.86 (0.57–1.31) 1919–1945 0.63 (0.41–0.98) 0.83 (0.52–1.31) 1945–1965 1.04 (0.71–1.51) 1.13 (0.76–1.69) 1965–1980 0.81 (0.54–1.21) 0.97 (0.64–1.47) 1980–2002 1.00 1.00 2(4 df) 15.5 (p⫽0.004) 4.88 (p⫽0.30) Townsend score 1.07 (1.03–1.11) 1.06 (1.01–1.10) 2(1 df) 15.1 (p⬍0.001) 6.67 (p⫽0.01) Floor area Small 1.41 (1.10–1.82) 1.37 (1.01–1.87) Medium 1.15 (0.88–1.50) 1.20 (0.88–1.63) Large 0.98 (0.75–1.29) 1.03 (0.77–1.38) Very large 1.00 1.00 2(3 df) 11.2 (p⫽0.01) 5.67 (p⫽0.13)
insights into some aspects of the relationship between the built environment and human health.
In this population-based study of the built environ-ment and human health the observed risk of injury in apartment buildings was substantially elevated. Addi-tional research is required to determine the relative contributions of the built environment and human behavior and psychology that may mediate this risk. Such research requires large numbers of subjects with measurement of both personal and environmental risk factors and would best be carried out in diverse settings in a multinational study. Studies such as these are needed by policymakers in order to develop health-based housing standards and building codes. In light of these findings and those of other studies, central and local government in the United Kingdom should review the appropriateness of existing housing standards and regulations and the degree to which these are en-forced. Local administrations in the United Kingdom wishing to prevent home injuries could start by target-ing apartment buildtarget-ings.
This study was sponsored by the Medical Research Council: A cross-disciplinary framework for evaluating the influence of the built environment on the health of the public. Medical Research Council, grant no. G9900679.
No financial conflict of interest was reported by the authors of this paper.
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