In the Medicare analysis, risk of bone fracture admissions at osteoporosis-related sites was greater in areas with higher PM 2·5 concentrations (risk ratio [RR] 1·041, 95% CI 1·030 to 1·051). This risk was particularly high among low-income communities (RR 1·076, 95% CI 1·052 to 1·100). In the longitudinal BACH/Bone study, baseline black carbon and PM 2·5 concentrations were associated with lower serum parathyroid hormone (β=–1·16, 95% CI −1·93 to −0·38, p=0·004, for 1 IQR increase [0·106 μg/m 3 ] in the 1-year average of black carbon concentrations; β=–7·39, 95% CI −14·17 to −0·61, p=0·03, for 1 IQR increase [2·18 μg/m 3 ] in the 1-year average of PM 2·5 concentrations). Black carbon concentration was associated with higher bone mineral density loss over time at multiple anatomical sites, including femoral neck (−0·08% per year for 1 IQR increase, 95% CI −0·14 to −0·02) and ultradistal radius (−0·06% per year for 1 IQR increase, −0·12 to −0·01). Black carbon and PM 2·5 concentrations were not associated with serum calcium or serum 25(OH)D concentrations.

In the first study, we examined the association of long-term concentrations of particulate matter less than 2·5 μm (PM 2·5 ) and osteoporosis-related fracture hospital admissions among 9·2 million Medicare enrollees (aged ≥65 years) of the northeast-mid-Atlantic USA between January, 2003, and December, 2010. In the second study, we examined the association of long-term black carbon and PM 2·5 concentrations with serum calcium homoeostasis biomarkers (parathyroid hormone, calcium, and 25-hydroxyvitamin [25(OH)D]) and annualised bone mineral density over 8 years (baseline, November, 2002–July, 2005; follow-up, June, 2010–October, 2012) of 692 middle-aged (46·7 years [SD12·3]), low-income men from the Boston Area Community Health/Bone Survey (BACH/Bone study) cohort. PM 2·5 concentrations were estimated using spatiotemporal hybrid modelling including Aerosol Optical Depth data, spatial smoothing, and local predictors. Black carbon concentrations were estimated using spatiotemporal land-use regression models.

Air particulate matter is a ubiquitous environmental exposure associated with oxidation, inflammation, and age-related chronic disease. Whether particulate matter is associated with loss of bone mineral density and risk of bone fractures is undetermined. We did two independent studies with complementary designs, objectives, and measures to determine the relationship between ambient concentrations of particulate matter and bone health.

To determine the relationship between ambient concentrations of particulate matter and bone health, we did two independent studies with complementary designs, objectives, and measures: using data on 763 630 hospital admissions from 9·2 million Medicare enrollees in the northeast-mid-Atlantic USA from 2003 to 2010, we determined whether communities with higher concentrations of particulate matter less than 2·5 μm in aerodynamic diameter (PM 2·5 ) had higher rates of hospital admissions for osteoporosis-related bone fractures among older persons (≥65 years old); in a longitudinal study of 692 middle-aged (mean age 47·5 years [SD 12·8]), low-income men from the Boston Area Community Health/Bone Survey cohort (BACH/Bone study), we determined whether PM 2·5 concentrations and traffic-derived ambient particulate matter—as traced through ambient concentrations of black carbon—were associated with altered markers of calcium homoeostasis, including serum parathyroid hormone, 25-hydroxyvitamin D (25(OH)D), and calcium, as well as changes in bone mineral density over approximately 8 years of follow-up.

This study provides evidence that long-term exposure to particulate matter—a persistent environmental issue in Europe and globally—is an independent risk factor for bone fractures, possibly involving changes in parathyroid hormone concentrations. These associations might disproportionately affect under-privileged communities. We found the association of particulate matter well below the annual average limits set by the US Environmental Protection Agency and the European Union. Improvements in particulate air pollution concentrations might ameliorate bone health, prevent bone fractures, and reduce the health cost burden associated with fractures in older individuals.

We demonstrate for the first time higher rates of hospital admissions for bone fractures in communities with higher ambient concentrations of particulate matter less than 2·5 μm in aerodynamic diameter. Participants living at addresses with higher concentrations of traffic-derived particulate matter exhibit lower serum parathyroid hormone concentrations and higher decreases in bone mineral density over an 8-year follow-up.

Exposure to particulate matter induces oxidative damage and inflammation, which might affect bone health, particularly of older populations. Smoking, which contains several components of particulate matter, has been consistently associated with bone damage. However, whether ambient particulate matter concentrations affect calcium metabolism, bone damage, and risk of fractures is uncertain.

Ambient concentrations of particulate matter air pollution have been associated with increased morbidity, hospitalisation, and mortality from cardiovascularand respiratory diseases,as well as with cancerand impaired cognition.Particulate matter causes systemic oxidative damageand inflammation,which can result in accelerated bone loss and increased risk of bone fractures in older individuals. Tobacco smoke, which contains several toxic components also found in particulate matter, has been repeatedly associated with decreased bone mineral densityand increased risk of bone fractures.However, evidence on whether individuals living in areas with higher concentrations of particulate matter have higher risk of bone fractures is inconclusive. No longitudinal study has investigated ambient particulate matter in relation to bone mineral loss over time, and there is no available data on particulate matter and calcium homoeostasis in adults.

In the USA, about 2·1 million osteoporosis-related bone fractures are reported each year, resulting in as much as US$20·3 billion in annual direct health costs.Within 1 year of a bone fracture, death risks for older individuals increase by 10–20%with only 40% regaining full pre-fracture independence.Identification of novel, preventable risk factors for bone loss and fractures is an urgent global priority.

The sponsors of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

We used linear regression to estimate the association of long-term PMand black carbon concentrations (1-year average PMand black carbon concentrations before bone mineral density measurement) with baseline parathyroid hormone, calcium, and serum 25(OH)D concentrations. We used three sets of models: unadjusted; adjusted for age, race, and height; and adjusted for age, race, height, smoking, per-capita household income, physical activity, caffeine consumption, and weight. We used similar sets of linear regression models to evaluate the association of baseline PMand black carbon concentrations (1-year average) with change in bone mineral density between baseline and approximately 8-year examinations.We rescaled the effect estimate to percent change to facilitate comparison of results with previous studies.SUDAAN software (RTI International, Research Triangle Park, NC, USA) was used for all analyses. Observations were weighted inversely to their probability of selection at baseline. Weights were also adjusted for non-response bias at the follow-up assessment and post-stratified to the Boston census population in 2000 ( appendix p 8 ). The multivariate imputation by chained equations (MICE) algorithm in R was used to impute missing data,taking into account the complex survey sampling design and maintaining the observed relationships in the data. MICE imputes missing values with estimated predictions from regression models and 15 datasets were multiply imputed and used for analysis.Imputed missing data were less than 5% per variable. p<0·05 was considered significant.

To estimate PMconcentrations, we used the same spatiotemporal hybrid modelling approachdescribed for the Medicare analysis, but using a 1 × 1 km model instead of zip code areas, which allowed more precise data about exposure.Due to the unavailability of 1 × 1 km satellite data before 2003, PMpredictions could be obtained—as an annual average—only for participants with baseline visits in 2004–05 (ie, only 282 of the total 692 participants). We obtained finer-scale and more complete (n=692) estimates of particle concentrations by calculating concentrations of black carbon—a measure of particulate matter from vehicular traffic emissions and the dominant type of particulate matter in urban areas—using a validated spatiotemporal land-use regression model that provided daily estimates of black carbon concentrations throughout the greater Boston area since 1995, as previously reported.To capture large local variability of vehicular traffic particles, the black carbon model generated estimates for each individual address rather than for grid cells. We calculated 1-year averages of PMand black carbon at baseline using 365 daily estimates for each participant using their residential address before the date of their baseline bone mineral density assessment.

We measured serum bio-intact parathyroid hormone, serum calcium at baseline, and serum 25(OH)D (ie, 25(OH)D+25(OH)D), as previously described.Trained and certified technicians measured bone mineral density at both baseline and follow-up at five different locations (femoral neck, total hip, lumbar spine [L1–L4], distal radius, ultradistal radius) with dual-energy x-ray absorptiometry (DXA) using a Hologic/QDR4500W densitometer (Hologic Inc, Waltham, MA, USA). To facilitate study operations, and in consideration of the 6-year lag between the baseline and follow-up measure, we did not require the same technician to do the two bone mineral density scans on each participant. However, all technicians were specifically trained and certified to use standardised procedures to reduce between-operator variability. Unfortunately, no measures of operator variability were collected. However, the total variability was very small: indeed, to reduce technical variability in bone mineral density measurements, the DXA system was monitored weekly for drift and the coefficient of variations for bone mineral density were less than 1·5%. We calculated annualised changes in bone mineral density between baseline and follow-up scans, and we calculated annualised change in percentage from the difference between the first and last measurement.

Smoking was determined using data from in-person interviews; the questionnaires assessed whether men had smoked at least 100 cigarettes in their lifetime and whether they were currently smoking. Smoking status was defined as current smoker (smoked >100 cigarettes and currently a smoker), never smokers (smoked <100 cigarettes lifetime and not currently smoking), or former smokers (smoked >100 cigarettes lifetime and currently not smoking). In the case of former and current smokers, questions were administered to determine the usual number of cigarettes smoked per day and for how many years they had smoked; then, pack-years of smoking were calculated by multiplying the number of packs (20 cigarettes in one pack) smoked per day by the number of years smoked. Additional information about the BACH/Bone cohort has been published previously.

Physical activity level was measured using the Physical Activity Scale for the Elderly (PASE).Frequency and duration of leisure activities, work (hours per week), and housework and similar duties (yes or no) over the previous week were recorded for each participant. The PASE score was computed by multiplying the amount of time spent in each activity (hours per week) in each activity by empirical item weights and summing over all activities. PASE measurements were categorised as low (0–99), middle (100–249), and high (≥250). Measurements of participants' height and weight were obtained using a stadiometer (Seca Corporation, Hanover, MD, USA) and digital scale (Tanita, Arlington Heights, IL, USA), respectively. Body-mass index (BMI) was calculated by dividing measured weight (kg) by the square of measured height (m). Information about dietary habits was obtained by survey in participants' homes using the Block food frequency questionnaire.

The BACH/Bone study is a population-based longitudinal study of musculoskeletal health, including 1219 low-income black, Hispanic, and white male residents of Greater Boston, MA, USA, aged 30–79 years.Data were collected at baseline (November, 2002–July, 2005) and follow-up (June, 2010–October, 2012) examinations from a total of 692 participants, who completed follow-up assessments.

We estimated the association of 1-year PMaverages with annual rates of bone fracture hospital admissions using generalised linear mixed models (PROC GLIMMIX; SAS Institute, Cary, NC, USA) with Poisson distribution and random intercepts for zip code. We considered the Akaike information criterion and residuals' plots to evaluate goodness-of-fit. We adjusted the final model for the multiple zip code-level confounders described in the appendix (p 6) . We used Medicare data on age that provides per each zip code the percentage of the population aged between 65 and 74 years and the percentage older than 75 years. We also adjusted for number of days below 0°C to minimise the potential impact of fall risk due to freezing weather. Urban and rural areas were classified according to the Rural–Urban commuting area from the US Department of Agriculture, which classifies US census tracts using measures of population density, urbanisation, and daily commuting. In separate models, we tested interaction terms between zip code characteristics and PMconcentrations. p<0·05 was considered significant.

Annual PMconcentrations between 2003 and 2010 were estimated using a recently developed and validated (mean out-of-sample R=0·88) spatio-temporal prediction model that incorporates satellite aerosol optical depth data, spatial smoothing, and local predictors.We generated daily PMpredictions at 1 × 1 km spatial resolution, as previously describedand calculated 1-year averages of PMconcentrations specific to each zip code for each calendar year. The exposure dataset with yearly averages of PMconcentrations at a 1 × 1 km spatial resolution was matched to zip codes using ArcGIS (a geographic information system) and SAS based on spatial location and date. For zip codes that covered several grids, a weighted exposure average was calculated for each zip code based on all covered 1 × 1 km grid cells.

We obtained 2003–10 hospital admission data for osteoporosis-related bone fractures from approximately 9·2 million beneficiaries of Medicare, aged 65 years or older, who lived in 3974 zip codes of 13 northeast-mid-Atlantic US states located east of the 81° W meridian, for which we recently developed a high-resolution hybrid model for estimating PMconcentrations ( figure 1 ).We identified primary hospital admissions for osteoporotic-related fractures using the International Classification of Diseases, 9th revision ( appendix pp 2–5 ) and compiled data on number of admissions per year per zip code. Covariate data at the zip code level were collected from various sources (eg, 2000 Census, Centers for Disease Control and Prevention; appendix p 6 ) and presented in their original unit to ensure accuracy. Medicare data are previously collected administrative data and, therefore, did not require individual patient consent.

At baseline, bone mineral density measures of the BACH/Bone study participants were not associated with PMor black carbon concentrations at their residential address ( table 5 ). During the 8-year follow-up, participants living at locations with higher concentrations of ambient particles, particularly black carbon concentrations, showed higher loss of bone mineral density at multiple anatomical sites ( table 6 ). For each 1 IQR (0·106 μg/m) increase in 1-year black carbon concentration at baseline, participants had a 0·08% per year (95% CI −0·14 to −0·02; p=0·009) decrease in femoral neck bone mineral density and 0·06% per year (−0·12 to −0·01; p=0·04) decrease in ultradistal radius bone mineral density in fully adjusted models, equivalent to 3914 cases per year attributable to PM. Bone mineral density showed non-significant negative associations at one-third distal radius, total hip, and L1–L4 vertebrae ( table 6 ). Associations remained robust across alternative regression models ( table 6 ) and relatively linear despite some non-influential outliers ( appendix p 10 ). In the subset of participants with available PMdata (n=282), 1-year average PMconcentrations at baseline were negatively, but non-significantly, associated with changes in bone mineral density for most anatomical sites evaluated ( table 6 ).

Annualised percentage change in bone mineral density at five anatomical sites, from 2002–05 to 2010–12, associated with 1 IQR increase in 1-year average exposure to PM 2·5 (2·18 μg/m 3 , n=282) and black carbon concentrations (0·106 μg/m 3 , n=692).

* Annualised percentage change in bone mineral density at five anatomical sites, from 2002–05 to 2010–12, associated with 1 IQR increase in 1-year average exposure to PM 2·5 (2·18 μg/m 3 , n=282) and black carbon concentrations (0·106 μg/m 3 , n=692).

Regression coefficients for the association between a baseline 1 IQR increase in the 1-year average of PM 2·5 (2·18 μg/m 3 , n=282) and black carbon concentrations (0·106 μg/m 3 , n=692) and baseline bone mineral density in five anatomical sites in the BACH/Bone study cohort (n=692).

* Regression coefficients for the association between a baseline 1 IQR increase in the 1-year average of PM 2·5 (2·18 μg/m 3 , n=282) and black carbon concentrations (0·106 μg/m 3 , n=692) and baseline bone mineral density in five anatomical sites in the BACH/Bone study cohort (n=692).

Participants in the BACH/Bone study included men aged 30–79 years, including 523 (66·9%) participants with annual household income less than US$30 000 and 274 (39·6%) who were white. We present additional characteristics of the participants at baseline, including bone mineral density, PM, and black carbon concentrations in table 3 . Participants living in locations with higher concentrations of black carbon had lower concentrations of serum parathyroid hormone (β=–1·16, 95% CI −1·93 to −0·38, p=0·004 in the fully adjusted model for 1 IQR increase [0·106 μg/m] in the 1-year average of black carbon concentrations). PMalso showed a negative association with serum parathyroid concentrations (β estimate=–7·39, 95% CI −14·17 to −0·61, p=0·03 in the fully adjusted model for 1 IQR [2·18 μg/m] increase in the 1-year average of PMconcentrations). Black carbon and PMconcentrations were not associated with serum calcium or serum 25(OH)D concentrations and results were robust across alternative regression models ( table 4 ).

Regression coefficients for the association between 1 IQR increase in the baseline 1-year average of PM 2·5 (2·18 μg/m 3 , n=282) or black carbon (0·106 μg/m 3 , n=692) concentrations and calcium homoeostasis biomarkers (parathyroid hormone, serum vitamin D, and serum calcium).

* Regression coefficients for the association between 1 IQR increase in the baseline 1-year average of PM 2·5 (2·18 μg/m 3 , n=282) or black carbon (0·106 μg/m 3 , n=692) concentrations and calcium homoeostasis biomarkers (parathyroid hormone, serum vitamin D, and serum calcium).

In the Medicare analysis, the area included in the analysis had a total population of 62 million, of which about 9·2 million (about 15%) were Medicare beneficiaries. Characteristics of zip code areas are in the appendix (p 8) . From 2003 to 2010, 763 630 Medicare beneficiaries were admitted with a primary diagnosis of osteoporosis-related bone fracture. Communities with higher annual PMconcentrations had higher rates of bone fracture admissions in analyses controlling for relevant covariates. One IQR (4·18 μg/m) increase in PMwas associated with a 4·1 % (risk ratio [RR] 1·041, 95% CI 1·030 to 1·051; p=0·0001) higher rate of hospital admission for bone fracture ( table 1 ) in models adjusted for sociodemographic variables, geographical characteristics, obesity, number of days with freezing temperatures (<0°C), and calendar year. A plot of the corresponding partial residuals obtained from a model controlling for all covariates except PMdemonstrated a subtle, near-linear covariate-adjusted association between PMand rates of bone fracture admissions ( figure 2 ). The plot also showed that there remained substantial variability in bone fracture admission rates. Using a regression spline to fit PMconcentration in the multivariable-adjusted regression model, we confirmed that the relationship between PMand rates of bone fractures was nearly linear across the entire range of PMconcentrations (3–22 μg/m figure 2 ). The associations of PMwith bone fractures were robust and stable across six alternative regression models including different sets of covariates ( appendix p 9 ). Risk ratios were similar between women (RR 1·046, 95% CI 1·036 to 1·056; p=0·0002) and men (RR 1·037, 1·027 to 1·047; p=0·0008; table 1 ). The association of PMwith bone fracture admission rates was higher among those communities in the lowest obesity rate quartile (RR 1·105, 95% CI 1·080 to 1·129; p=0·0007) compared with those with highest obesity rates (RR 1·038, 0·763 to 1·312; p=0·13; p=0·011). The effect modification by socioeconomic variables (percentage of population with high school level per zip code and median income per zip code), percentage of population white, non-Hispanic per zip code, and percentage of obesity per zip code, on the association between long-term PMand hospital admissions by bone fractures are shown in table 2

Risk of hospital admissions by osteoporosis-related bone fracture associated with PM 2·5 concentrations in each quartile of socioeconomic status, race, and obesity in the Medicare analysis

Table 2 Risk of hospital admissions by osteoporosis-related bone fracture associated with PM 2·5 concentrations in each quartile of socioeconomic status, race, and obesity in the Medicare analysis

(A) Scatter plot of the multivariable-adjusted residuals from the standard regression model (not including PM 2·5 ) versus level of exposure to fine particulate matter less than 2·5 μm (PM 2·5 ). Blue line corresponds to zero partial residual value. (B) Spline for the multivariable-adjusted association between PM 2·5 exposure and number of hospital admissions of Medicare enrollees per zip code, from 2003 to 2010. Horizontal dotted line represents zero effect. (C) Density plot of exposure to PM 2·5 in the Medicare analysis. The vertical dotted line in B and C represents the primary annual PM 2·5 standard of 12 μg/m 3 mandated by the US Environmental Protection Agency.

In each zip code, age was reported in the Medicare data as the percentage of the population aged 65–74 years and the percentage aged 75 years or older.

§ In each zip code, age was reported in the Medicare data as the percentage of the population aged 65–74 years and the percentage aged 75 years or older.

Regression models also included indicator variables for year of hospital admission and state of residence, in addition to all the other independent variables listed.

‡ Regression models also included indicator variables for year of hospital admission and state of residence, in addition to all the other independent variables listed.

Estimated risk of hospital admissions of Medicare enrollees with a primary diagnosis of bone fracture associated with 1 IQR (4·18 μg/m 3 ) increase in 1-year average concentrations of fine particulate matter less than 2·5 μm (PM 2·5 ) across 3974 zip code areas in the northeast-mid-Atlantic area of the USA in the period 2003–10; adjusted estimates of each variable are presented.

† Estimated risk of hospital admissions of Medicare enrollees with a primary diagnosis of bone fracture associated with 1 IQR (4·18 μg/m 3 ) increase in 1-year average concentrations of fine particulate matter less than 2·5 μm (PM 2·5 ) across 3974 zip code areas in the northeast-mid-Atlantic area of the USA in the period 2003–10; adjusted estimates of each variable are presented.

Discussion

In our analysis of approximately 9·2 million Medicare beneficiaries, we found evidence of an association between PM 2·5 concentrations and rates of hospital admissions for bone fractures, independent of sex and community-level confounding factors. PM 2·5 associations were stronger in communities with lower income, despite a protective influence of obesity rates. This result suggests that per each 4·18 μg/m3 increase in PM 2·5 , there is a 4·1% higher rate of hospital admission for bone fractures in older individuals. In the prospective BACH/Bone study of middle-aged, low-income men, we also found that participants living at addresses with higher concentrations of PM 2·5 and black carbon exhibited lower serum parathyroid hormone concentrations. Black carbon was associated with decreases in bone mineral density over 8 years of follow-up. These findings indicate poor air quality as a possible risk factor for bone mineral density loss and fractures in older individuals, which might disproportionately affect low-income men. Reducing emissions as a result of innovation in technologies or policy changes in emission standards of this modifiable risk factor might reduce the impact of air pollution on bone fractures and osteoporosis.

34 Knaapen AM

Borm PJA

Albrecht C

Schins RPF Inhaled particles and lung cancer. Part A: mechanisms. 17 Bind M-A

Baccarelli A

Zanobetti A

et al. Air pollution and markers of coagulation, inflammation, and endothelial function: associations and epigene–environment interactions in an elderly cohort. 35 Smith BJ

Lerner MR

Bu SY

et al. Systemic bone loss and induction of coronary vessel disease in a rat model of chronic inflammation. 36 Ajiro Y

Tokuhashi Y

Matsuzaki H

Nakajima S

Ogawa T Impact of passive smoking on the bones of rats. 19 Law MR

Hackshaw AK A meta-analysis of cigarette smoking, bone mineral density and risk of hip fracture: recognition of a major effect. 37 Gunnarsson O

Indridason OS

Franzson L

Sigurdsson G Factors associated with elevated or blunted PTH response in vitamin D insufficient adults. , 38 Jorde R

Saleh F

Figenschau Y

Kamycheva E

Haug E

Sundsfjord J Serum parathyroid hormone (PTH) levels in smokers and non-smokers. The Fifth Tromsø Study. 38 Jorde R

Saleh F

Figenschau Y

Kamycheva E

Haug E

Sundsfjord J Serum parathyroid hormone (PTH) levels in smokers and non-smokers. The Fifth Tromsø Study. 38 Jorde R

Saleh F

Figenschau Y

Kamycheva E

Haug E

Sundsfjord J Serum parathyroid hormone (PTH) levels in smokers and non-smokers. The Fifth Tromsø Study. 39 Tamadon MR

Nassaji M

Ghorbani R Cigarette smoking and nephrolitiasis in adult individuals. , 40 Liu C-C

Huang S-P

Wu W-J

et al. The impact of cigarette smoking, alcohol drinking and betel quid chewing on the risk of calcium urolithiasis. Air particles might, directly or indirectly, impact bone biology and increase bone mineral loss. Air pollution particles have high potential to cause systemic oxidative damageand inflammation,both of which are established mechanisms for bone demineralisation and osteoporosis.Tobacco smoke, which includes several chemo-physical components found in particulate matter, causes bone mineral loss in animal experimentsand has been associated with higher risk of bone fractures and increased bone mineral loss in studies of human beings.Parathyroid hormone concentrations are also significantly lower in smokersand return to non-smoking concentrations after smoking cessation.Such parathyroid hormone alteration might represent an adaptive response to smoking-induced calcium mobilisation from bone.Our findings suggest that similar mechanisms might also be activated in response to particulate matter. Similarities between particulate matter and smoking might also suggest a potential role of renal calcium handling,but unfortunately no data about renal calcium were available in the BACH/Bone cohort.

2·5 concentrations with the prevalence of self-reported forearm fractures after the age of 50 years, but the association was evident only among male smokers. 41 Alver K

Meyer HE

Falch JA

Søgaard AJ Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study. 2·5 and PM 10 concentrations with lower total body bone mineral density. 42 Alvaer K

Meyer HE

Falch JA

Nafstad P

Søgaard AJ Outdoor air pollution and bone mineral density in elderly men—the Oslo Health Study. 43 Bjørgul K

Reikerås O Incidence of hip fracture in southeastern Norway: a study of 1,730 cervical and trochanteric fractures. , 44 Cooley HM

Jones G Symptomatic fracture incidence in southern Tasmania: does living in the country reduce your fracture risk?. 45 Omsland TK

Ahmed LA

Grønskag A

et al. More forearm fractures among urban than rural women: the NOREPOS study based on the Tromsø study and the HUNT study. 2·5 . The magnitude of the relative risk we found in the Medicare analysis is similar to the very well established associations between air pollution and other health outcomes (eg, myocardial infarction, stroke, and total mortality). 46 Nawrot TS

Perez L

Künzli N

Munters E

Nemery B Public health importance of triggers of myocardial infarction: a comparative risk assessment. 47 Baccarelli A

Benjamin EJ Triggers of MI for the individual and in the community. Very few studies have investigated the association of air pollution concentrations with bone health and bone fractures. A cross-sectional study of 5976 middle-aged and older individuals living in Norway (15·23% with forearm fractures, about 910 cases) described an association of long-term PMconcentrations with the prevalence of self-reported forearm fractures after the age of 50 years, but the association was evident only among male smokers.A previous study of 590 men aged 75–76 years showed a cross-sectional correlation of long-term PMand PMconcentrations with lower total body bone mineral density.Previous studies have also reported higher rates of bone fractures and age-related osteoporosis in urban areas compared with rural regions.For example, urban women have a 29% higher relative risk of forearm fracture and lower bone mineral density compared with women in rural areas.Our Medicare analysis controlled for urban and rural locations. It is possible that our study is prone to residual confounding. However, considering the consistency between different models ( appendix p 9 ), it is unlikely the observed association of particulate matter on hospital admissions by bone fractures reflects confounding due to lifestyle or other socioeconomic differences between urban and rural areas. We used yearly counts of admissions for each zip code area and specified a Poisson distribution. We applied generalised mixed models because we have counts for each zip code and then we included a random intercept for zip code to take into account the characteristics of each zip code. By using this Poisson regression, we accounted for temporal variation of counts by year and for the spatial variation with the zip code level. We did not evaluate daily time series because we were interested in the long-term effect of PM. The magnitude of the relative risk we found in the Medicare analysis is similar to the very well established associations between air pollution and other health outcomes (eg, myocardial infarction, stroke, and total mortality).Indeed, air pollution is considered a weak, but universal risk factor; therefore, it causes a proportionally higher number of attributable cases than other risk factors with higher relative risks but lower frequency.

48 Nam H-S

Kweon S-S

Choi J-S

et al. Racial/ethnic differences in bone mineral density among older women. , 49 Nam H-S

Shin M-H

Zmuda JM

et al. Race/ethnic differences in bone mineral densities in older men. 50 Douchi T

Yamamoto S

Oki T

et al. Difference in the effect of adiposity on bone density between pre- and postmenopausal women. , 51 Guney E

Kisakol G

Ozgen G

Yilmaz C

Yilmaz R

Kabalak T Effect of weight loss on bone metabolism: comparison of vertical banded gastroplasty and medical intervention. , 52 Radak TL Caloric restriction and calcium's effect on bone metabolism and body composition in overweight and obese premenopausal women. 53 Navarro MDC

Saavedra P

Jódar E

Gómez de Tejada MJ

Mirallave A

Sosa M Osteoporosis and metabolic syndrome according to socio-economic status, contribution of PTH, vitamin D and body weight: the Canarian Osteoporosis Poverty Study (COPS). 54 Freedman BI

Register TC Effect of race and genetics on vitamin D metabolism, bone and vascular health. 2·5 exposure with bone fracture admission rates was higher among those communities in the lowest obesity rate quartile compared with those with the highest obesity rates, suggesting a protective influence of obesity rates. Several epidemiological studies have shown that socioeconomic factors, race,and obesityare related to bone mineral density. Low socioeconomic status has been associated with 25(OH)D insufficiency, higher concentrations of parathyroid hormone, lower values of bone mineral density, and a higher prevalence of fragility fractures.Also, despite lower serum 25(OH)D concentrations and dietary calcium intake, African Americans have higher bone mineral density and develop osteoporosis less frequently than do European Americans.Our Medicare analysis showed a significant interaction of socioeconomic variables (education and income), but also of race and obesity, confirming those previous factors. For example, we found that the association of PMexposure with bone fracture admission rates was higher among those communities in the lowest obesity rate quartile compared with those with the highest obesity rates, suggesting a protective influence of obesity rates.

2·5 , is a tracer of particles from traffic and might share different toxicological properties compared with other components of particulate matter. Therefore, our results indicate that particles from traffic are crucial contributors to decreased bone health. PM 2·5 concentrations showed only weak and non-significant associations with both annualised changes in bone mineral density and serum parathyroid hormone concentrations. However, the PM 2·5 analysis included only about 40% of the BACH/Bone participants due to unavailability of PM 2·5 model predictions in the early years of the study. Lack of significance could be attributable to the lower number of middle-aged, low-income men—compared with the black carbon analysis—with long-term PM 2·5 data, but this result has the potential of selection bias for lack of data in the full BACH/Bone cohort. In the BACH/Bone study, we did not observe an association between long-term black carbon exposure and bone mineral density at baseline, but we found associations with yearly change between baseline and follow-up bone mineral density in the longitudinal analysis. Lack of association in the cross-sectional analysis of 1-year average black carbon exposure and bone mineral density at baseline might indicate that individuals are less susceptible to black carbon at a younger age and, consequently, effects were observed only as participants aged during the follow-up analysis. 55 Cashman KD Diet, nutrition, and bone health. During the 8-year follow-up in the BACH/Bone study, middle-aged, low-income men living at locations with higher concentrations of black carbon had larger annualised decreases in bone mineral density. Black carbon, a major component of fine particles measured by PM, is a tracer of particles from traffic and might share different toxicological properties compared with other components of particulate matter. Therefore, our results indicate that particles from traffic are crucial contributors to decreased bone health. PMconcentrations showed only weak and non-significant associations with both annualised changes in bone mineral density and serum parathyroid hormone concentrations. However, the PManalysis included only about 40% of the BACH/Bone participants due to unavailability of PMmodel predictions in the early years of the study. Lack of significance could be attributable to the lower number of middle-aged, low-income men—compared with the black carbon analysis—with long-term PMdata, but this result has the potential of selection bias for lack of data in the full BACH/Bone cohort. In the BACH/Bone study, we did not observe an association between long-term black carbon exposure and bone mineral density at baseline, but we found associations with yearly change between baseline and follow-up bone mineral density in the longitudinal analysis. Lack of association in the cross-sectional analysis of 1-year average black carbon exposure and bone mineral density at baseline might indicate that individuals are less susceptible to black carbon at a younger age and, consequently, effects were observed only as participants aged during the follow-up analysis.

42 Alvaer K

Meyer HE

Falch JA

Nafstad P

Søgaard AJ Outdoor air pollution and bone mineral density in elderly men—the Oslo Health Study. , 56 Meyer HE

Berntsen GKR

Søgaard AJ

et al. Higher bone mineral density in rural compared with urban dwellers: the NOREPOS study. , 57 Chang K-H

Chang M-Y

Muo C-H

et al. Exposure to air pollution increases the risk of osteoporosis: a nationwide longitudinal study. , 58 Chen Z

Salam MT

Karim R

et al. Living near a freeway is associated with lower bone mineral density among Mexican Americans. 58 Chen Z

Salam MT

Karim R

et al. Living near a freeway is associated with lower bone mineral density among Mexican Americans. 57 Chang K-H

Chang M-Y

Muo C-H

et al. Exposure to air pollution increases the risk of osteoporosis: a nationwide longitudinal study. 59 Kanis JA

Odén A

Johnell O

Jónsson B

de Laet C

Dawson A The burden of osteoporotic fractures: a method for setting intervention thresholds. 42 Alvaer K

Meyer HE

Falch JA

Nafstad P

Søgaard AJ Outdoor air pollution and bone mineral density in elderly men—the Oslo Health Study. We observed a negative association between long-term black carbon exposure and reduction in femoral neck and ultradistal radius bone mineral density. Although non-significant, negative associations between black carbon and one-third distal radius, total hip, and lumbar vertebral bone mineral density were also observed. Our study is consistent with the finding that air pollution contributes to bone health impairment reported by different groups.Chen and colleaguesshowed that traffic-related exposure was associated with lower body bone mineral density. Also, Chang and colleaguesfound an association between air pollution (carbon monoxide and nitrogen dioxide) and increased risk of osteoporosis. The difference in observed associations across multiple anatomical sites might be explained by differential anatomical susceptibility to the effects of particulate matter on bones.Alvaer and colleaguesreported sex differences in the association between air pollution and bone mineral density, with an association observed only for men. However, our finding from the Medicare analysis suggested that the impacts of ambient particulate air pollution on bone health might not be different between men and women. The difference in conclusion and findings between our study and the Oslo Health Study might be explained by age differences of the participants. This finding suggests that the potential adverse consequences of ambient particulate air pollution on bone health might be similar in men and women.

2·5 exposure with hospital admissions at the individual level. To avoid the potential ecological fallacy, 60 Piantadosi S

Byar DP

Green SB The ecological fallacy. The two studies reported in this paper have notable limitations. The Medicare analysis used an ecological design and has limited capability of establishing causality. The analysis was done at zip code level and does not allow for evaluating the association of long-term PMexposure with hospital admissions at the individual level. To avoid the potential ecological fallacy,we complemented the Medicare analysis with the BACH/Bone study to investigate the impact of individual-level environmental risk factors on bone health. However, the Medicare analysis included a large number of hospital admissions for osteoporotic-related bone fractures in older individuals, over a large and heterogeneous geographical region in the USA. The Medicare analysis might also be subject to selection bias, which is always a concern in observational studies. However, all individuals aged 65 years or older are encouraged to enrol in the free Medicare programme. Based on the enrolment criteria of Medicare beneficiaries, we assume that the Medicare enrollees are representative of the ageing population in the northeast-mid-Atlantic USA. We acknowledge a major limitation in that the hospital admission data were not validated, therefore we cannot exclude coding errors. However, based on study operations, misclassification is unlikely to be differential in areas with low and high particulate matter concentrations. Therefore, coding errors are likely to result by non-differential measurement error, and are expected to bias the association towards the null rather than producing the observed associations. Furthermore, although our Medicare analysis was adjusted for risk factors of fractures at the zip code level, there are other known risk factors for falls and bone fractures that were not available from Medicare data. However, most factors were accounted for at the individual level in the BACH/Bone study. Therefore, combining the two studies limits concerns about population-level analysis and bias from known confounders.

41 Alver K

Meyer HE

Falch JA

Søgaard AJ Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study. 2·5 with bone fracture rates in both men and women, which strengthened and complemented the findings from the BACH/Bone study. However, we assigned the closest particulate matter exposure available both in the Medicare study as in the BACH/Bone study but long-term particulate matter exposure was not directly measured and no personal data were available. Also, our results could be influenced by other unmeasured individual factors, such as ultraviolet exposure or calcium intake, among others, that can modify bone health and that were not evaluated here. Likewise, although our models used specifically concentrations of PM 2·5 (for the Medicare study and for a subset of participants in the BACH/Bone study) and black carbon (for the BACH/Bone study), we cannot exclude that the effect we observed might be mediated by other air pollutants or by the combination of them. Additionally, the DXA-based bone mineral density measures used might not detect microstructural alterations that are not readily apparent. Therefore, bone mineral density might fail to fully capture alterations related to bone health. 61 Fonseca H

Moreira-Gonçalves D

Coriolano H-JA

Duarte JA Bone quality: the determinants of bone strength and fragility. 42 Alvaer K

Meyer HE

Falch JA

Nafstad P

Søgaard AJ Outdoor air pollution and bone mineral density in elderly men—the Oslo Health Study. , 57 Chang K-H

Chang M-Y

Muo C-H

et al. Exposure to air pollution increases the risk of osteoporosis: a nationwide longitudinal study. 2.5 and black carbon, as well as of carbon monoxide and nitrogen dioxide on bone health are warranted. Finally, although we have adjusted for multiple potential confounders (smoking, race, physical activity, and income) in the BACH/Bone study, our results might not be sufficient to rule out selection bias, especially in the PM 2·5 model in which the number of participants was low. We acknowledge that the analysis done in the BACH/Bone study has several limitations due to moderate sample size and lack of generalisability, given that the cohort included 692 men only. However, to the best of our knowledge, only one study has reported the association between particulate matter exposure and bone fractures.Nonetheless, the BACH/Bone study is distinctly unique because of the prospective bone mineral density assessment at two timepoints. Furthermore, in the Medicare analysis, we found similar associations of PMwith bone fracture rates in both men and women, which strengthened and complemented the findings from the BACH/Bone study. However, we assigned the closest particulate matter exposure available both in the Medicare study as in the BACH/Bone study but long-term particulate matter exposure was not directly measured and no personal data were available. Also, our results could be influenced by other unmeasured individual factors, such as ultraviolet exposure or calcium intake, among others, that can modify bone health and that were not evaluated here. Likewise, although our models used specifically concentrations of PM(for the Medicare study and for a subset of participants in the BACH/Bone study) and black carbon (for the BACH/Bone study), we cannot exclude that the effect we observed might be mediated by other air pollutants or by the combination of them. Additionally, the DXA-based bone mineral density measures used might not detect microstructural alterations that are not readily apparent. Therefore, bone mineral density might fail to fully capture alterations related to bone health.We also acknowledge potential misclassification, especially in the BACH/Bone study, but this is likely to be non-differential (ie, the measurement error of exposure in the BACH/Bone study is unlikely to be dependent on bone mineral density status), therefore it is expected to bias our results towards the null. Other air pollutants such as carbon monoxide and nitrogen dioxide have been previously associated with bone loss and osteoporosis.Unfortunately, we did not have access to carbon monoxide and nitrogen dioxide exposure data for the Medicare study nor for the BACH/Bone study, limiting our capability to explore these associations. Further analyses to evaluate the role of PMand black carbon, as well as of carbon monoxide and nitrogen dioxide on bone health are warranted. Finally, although we have adjusted for multiple potential confounders (smoking, race, physical activity, and income) in the BACH/Bone study, our results might not be sufficient to rule out selection bias, especially in the PMmodel in which the number of participants was low.