The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.

BACKGROUND: Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. METHODS AND FINDINGS: We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. CONCLUSIONS: In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service.

BACKGROUND: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Early life PM2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland.

BACKGROUND: Living in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood. METHODS: Data were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants' residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability. RESULTS: The final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01-1.04 per 10 µg m-3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02-1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02-1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 µg m-3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations. CONCLUSIONS: In our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.

Lower social participation partly explains the association between perceived neighbourhood crime and depressive symptoms in European adults aged 50 years or older: A longitudinal mediation analysis.

Neighbourhood crime likely increases the risk of developing depression among older adults. However, little is known about the underlying behavioural and social pathways. We examined the association between perceived neighbourhood crime and depressive symptoms and whether this relationship was mediated by health behaviours (physical activity, smoking, and alcohol consumption) and social participation. Furthermore, we explored differential vulnerability across age, gender, education and household wealth. Data were drawn from six waves of longitudinal data (from 2004/2005 to 2017) of approximately 15,000 adults aged 50 years and older, derived from the multi-national Survey of Health, Ageing and Retirement in Europe. Perceived neighbourhood crime and covariates were measured at baseline, time-variant mediators and depressive symptoms across all waves. Confounder-adjusted mediator and outcome models were fitted with mixed-effects models. Total association was decomposed into direct and indirect pathways applying causal mediation analyses with Monte-Carlo simulations. Perceived crime was associated with higher risk of depressive symptoms; 4.6% of the effect was mediated via lower engagement in social activities (b = 0.005; 95% CI: 0.001-0.009). No mediation was detected through physical activity, smoking or alcohol consumption. Exploratory analyses revealed that the mediating role of social participation was more pronounced among participants with low household wealth (b = 0.012; 95% CI: 0.004-0.023; 7.3% mediated). Lower engagement in social activities partly explained the association between perceived neighbourhood crime and depressive symptoms in adults aged 50 years or older. Policies targeting disadvantaged communities to prevent crime and support social participation might be beneficial for population mental health, especially among financially vulnerable older residents.

A data linkage study of the effects of the Great Recession and austerity on antidepressant prescription usage.

BACKGROUND: International literature shows unemployment and income loss during the Great Recession worsened population mental health. This individual-level longitudinal study examines how regional economic trends and austerity related to depression using administrative prescription data for a large and representative population sample. METHODS: Records from a sample of the Scottish Longitudinal Study (N=86 500) were linked to monthly primary care antidepressant prescriptions (2009-15). Regional economic trends were characterized by annual full-time employment data (2004-14). Economic impact of austerity was measured via annual income lost per working age adult due to welfare reforms (2010-15). Sequence analysis identified new cases of antidepressant use, and group-based trajectory modelling classified regions into similar economic trajectories. Multi-level logistic regression examined relationships between regional economic trends and new antidepressant prescriptions. Structural equation mediation analysis assessed the contributory role of welfare reforms. RESULTS: Employed individuals living in regions not recovering post-recession had the highest risk of beginning a new course of antidepressants (AOR 1.23; 95% CI 1.08-1.38). Individuals living in areas with better recovery trajectories had the lowest risk. Mediation analyses showed that 50% (95% CI 7-61%) of this association was explained by the impact of welfare benefit reforms on average incomes. CONCLUSIONS: Following the Great Recession, local labour market decline and austerity measures were associated with growing antidepressant usage, increasing regional inequalities in mental health. The study evidences the impact of austerity on health inequalities and suggests that economic conditions and welfare policies impact on population health. Reducing the burden of mental ill-health primarily requires action on the social determinants.

The Longitudinal Associations of Perceived Neighborhood Disorder and Lack of Social Cohesion With Depression Among Adults Aged 50 Years or Older: An Individual-Participant-Data Meta-Analysis From 16 High-Income Countries.

Although residential environment might be an important predictor of depression among older adults, systematic reviews point to a lack of longitudinal investigations, and the generalizability of the findings is limited to a few countries. We used longitudinal data collected between 2012 and 2017 in 3 surveys including 15 European countries and the United States and comprising 32,531 adults aged 50 years or older. The risk of depression according to perceived neighborhood disorder and lack of social cohesion was estimated using 2-stage individual-participant-data meta-analysis; country-specific parameters were analyzed by meta-regression. We conducted additional analyses on retired individuals. Neighborhood disorder (odds ratio (OR) = 1.25) and lack of social cohesion (OR = 1.76) were significantly associated with depression in the fully adjusted models. In retirement, the risk of depression was even higher (neighborhood disorder: OR = 1.35; lack of social cohesion: OR = 1.93). Heterogeneity across countries was low and was significantly reduced by the addition of country-level data on income inequality and population density. Perceived neighborhood problems increased the overall risk of depression among adults aged 50 years or older. Policies, especially in countries with stronger links between neighborhood and depression, should focus on improving the physical environment and supporting social ties in communities, which can reduce depression and contribute to healthy aging.

The long-term health effects of attending a selective school: a natural experiment.

BACKGROUND: Education is widely associated with better physical and mental health, but isolating its causal effect is difficult because education is linked with many socioeconomic advantages. One way to isolate education's effect is to consider environments where similar students are assigned to different educational experiences based on objective criteria. Here we measure the health effects of assignment to selective schooling based on test score, a widely debated educational policy. METHODS: In 1960s Britain, children were assigned to secondary schools via a test taken at age 11. We used regression discontinuity analysis to measure health differences in 5039 people who were separated into selective and non-selective schools this way. We measured selective schooling's effect on six outcomes: mid-life self-reports of health, mental health, and life limitation due to health, as well as chronic disease burden derived from hospital records in mid-life and later life, and the likelihood of dying prematurely. The analysis plan was accepted as a registered report while we were blind to the health outcome data. RESULTS: Effect estimates for selective schooling were as follows: self-reported health, 0.1 worse on a 4-point scale (95%CI - 0.2 to 0); mental health, 0.2 worse on a 16-point scale (- 0.5 to 0.1); likelihood of life limitation due to health, 5 percentage points higher (- 1 to 10); mid-life chronic disease diagnoses, 3 fewer/100 people (- 9 to + 4); late-life chronic disease diagnoses, 9 more/100 people (- 3 to + 20); and risk of dying before age 60, no difference (- 2 to 3 percentage points). Extensive sensitivity analyses gave estimates consistent with these results. In summary, effects ranged from 0.10-0.15 standard deviations worse for self-reported health, and from 0.02 standard deviations better to 0.07 worse for records-derived health. However, they were too imprecise to allow the conclusion that selective schooling was detrimental. CONCLUSIONS: We found that people who attended selective secondary school had more advantaged economic backgrounds, higher IQs, higher likelihood of getting a university degree, and better health. However, we did not find that selective schooling itself improved health. This lack of a positive influence of selective secondary schooling on health was consistent despite varying a wide range of model assumptions.

Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health.

BACKGROUND: Over the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. METHODS: National Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N = 151,418). Antidepressant prescription status over the previous 6 months was recorded for every month for which data were available (January 2009-December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. RESULTS: Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to be in the group that started a new course of antidepressant prescriptions. CONCLUSIONS: The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

A longitudinal study of neighbourhood conditions and depression in ageing European adults: Do the associations vary by exposure to childhood stressors?

Emerging literature emphasises the association between neighbourhood conditions and late life depression. Childhood experiences, crucial for life course development of mental health, may modify how neighbourhood affects subsequent depression. This study assessed the longitudinal associations of access to services and neighbourhood nuisance with depression among older adults, and tested whether these associations varied by exposure to childhood stressors. Data were drawn from the cross-national Survey of Health, Ageing and Retirement in Europe, a prospective cohort study between 2004/2005 and 2015, representative for European adults over the age of 50. Individual perceptions of neighbourhood were measured at baseline; childhood stressors, defined as socioeconomic conditions, adverse experiences and health problems, were collected retrospectively. Multilevel logistic regression estimated the risk of depression (n = 10,328). Access to services were negatively (OR = 0.78, 95% CI 0.68-0.90) and neighbourhood nuisance positively (OR = 1.36, 95% CI 1.18-1.56) associated with the probability of depression during follow-up. We found interactions between neighbourhood and childhood socioeconomic conditions, but not with adverse experiences and health problems. While older adults who grew up in better childhood socioeconomic conditions benefited more from living in a residential area with good access to services, they were at higher risk of developing depression when residing in areas with more neighbourhood nuisances. Older adults' mental health can benefit from better access to public transportation and neighbourhood amenities, while physical and social problems in the local area increase the risk of depression. Importantly, socioeconomic circumstances in early life may influence vulnerability to neighbourhood effects in older age.

Neighbourhood tobacco supply and individual maternal smoking during pregnancy: a fixed-effects longitudinal analysis using routine data.

BACKGROUND: Tobacco policy is increasingly focusing on the 'tobacco endgame' which commits to eradicating tobacco use (prevalence below 5%) within the next two decades. Strategies for achieving the endgame are likely to include addressing the supply of tobacco products, yet current evidence to support this approach is primarily cross-sectional. METHODS: We use longitudinal smoking information from routine maternity records of all women who gave birth in Scotland between 2000 and 2015. We linked this data to the residential density of retailers selling tobacco products and the neighbourhood prevalence of smoking during pregnancy. In the analysis, individual mothers act as their own controls because we compare changes in their smoking behaviour between pregnancies to changes in exposure to tobacco retailing that arises from residential movement between pregnancies. RESULTS: Adjusted ORs showed an increased risk of being a smoker associated with increases in exposure to retailer density (OR 1.67, 95% CI 1.27 to 2.20). CONCLUSIONS: The results provide the strongest evidence to date of an association between the neighbourhood availability of tobacco and smoking, and the first to do so among pregnant women. These findings provide supportive evidence for interventions targeting the supply of tobacco products in achieving the endgame.

GUILD: GUidance for Information about Linking Data sets.

Record linkage of administrative and survey data is increasingly used to generate evidence to inform policy and services. Although a powerful and efficient way of generating new information from existing data sets, errors related to data processing before, during and after linkage can bias results. However, researchers and users of linked data rarely have access to information that can be used to assess these biases or take them into account in analyses. As linked administrative data are increasingly used to provide evidence to guide policy and services, linkage error, which disproportionately affects disadvantaged groups, can undermine evidence for public health. We convened a group of researchers and experts from government data providers to develop guidance about the information that needs to be made available about the data linkage process, by data providers, data linkers, analysts and the researchers who write reports. The guidance goes beyond recommendations for information to be included in research reports. Our aim is to raise awareness of information that may be required at each step of the linkage pathway to improve the transparency, reproducibility, and accuracy of linkage processes, and the validity of analyses and interpretation of results.

A cancer geography paradox? Poorer cancer outcomes with longer travelling times to healthcare facilities despite prompter diagnosis and treatment: a data-linkage study.

BACKGROUND: Rurality and distance from cancer treatment centres have been shown to negatively impact cancer outcomes, but the mechanisms remain obscure. METHODS: We analysed the impact of travel time to key healthcare facilities and mainland/island residency on the cancer diagnostic pathway (treatment within 62 days of referral, and within 31 days of diagnosis) and 1-year mortality using a data-linkage study with 12 339 patients. RESULTS: After controlling for important confounders, mainland patients with more than 60 min of travelling time to their cancer treatment centre ((OR 1.42; 95% CI 1.25-1.61) and island dwellers (OR 1.32; 95% CI 1.09-1.59) were more likely to commence cancer treatment within 62 days of general practitioner (GP) referral and within 31 days of their cancer diagnosis compared with those living within 15 min. Island-dweller patients were more likely to have their diagnosis and treatment started on the same or next day (OR 1.72; 95% CI 1.31-2.25). Increased travelling time to a cancer treatment centre was associated with increased mortality to 1 year (30-59 min (HR 1.21; 95% CI 1.05-1.41), >60 min (HR 1.18; 95% CI 1.03-1.36), island dweller (HR 1.17; 95% CI 0.97-1.41). CONCLUSIONS: Island dwelling and greater mainland travel burden was associated with more rapid cancer diagnosis and treatment following GP referral even after adjustment for advanced disease; however, these patients also experienced a survival disadvantage compared with those living nearer. Cancer services may need to be better configured to suit the different needs of dispersed populations.

Socioeconomic disadvantage, fetal environment and child development: linked Scottish administrative records based study.

BACKGROUND: Cognitive development in childhood is negatively affected by socioeconomic disadvantage. This study examined whether differences in fetal environment might mediate the association between family socioeconomic position and child development. METHODS: Data were linked from the Scottish Longitudinal Study, maternity inpatient records and the Child Health Surveillance Programme - Pre School for 32,238 children. The outcome variables were based on health visitor assessment of gross motor, hearing and language, vision and fine motor, and social development. Socioeconomic position was measured using parental social class and highest qualification attained. Random-effects logistic regression models were estimated to account for multiple reviews and familial clustering. Mediation analysis was conducted using the Karlson-Holm-Breen method. RESULTS: Hearing and language, vision and fine motor, and social development were associated with lower parental social class and lower parental educational qualifications after adjustment for fetal environment. Fetal environment partially mediated the estimated effect of having parents without educational qualifications for hearing and language (ß = 0·15; 95% confidence interval (CI) = 0·07, 0·23), vision and fine motor (ß = 0·19; CI = 0·10, 0·28) and social development (ß = 0·14; CI = 0·03 to 0·25). CONCLUSIONS: Socioeconomic position predicted hearing and language, vision and fine motor, and social development but not gross motor development. For children of parents without educational qualifications, fetal environment appears to contribute to a part of the socioeconomic gradient in child development abnormalities but post-natal environment appears to still explain the majority of the gradient and for other children most of it.

Pregnancy outcome and ultraviolet radiation; A systematic review.

BACKGROUND: Season and vitamin D are indirect and direct correlates of ultraviolet (UV) radiation and are associated with pregnancy outcomes. Further to producing vitamin D, UV has positive effects on cardiovascular and immune health that may support a role for UV directly benefitting pregnancy. OBJECTIVES: To investigate the effects of UV exposure on pregnancy; specifically fetal growth, preterm birth and hypertensive complications. METHODS: We conducted a systematic review of Medline, EMBASE, DoPHER, Global Health, ProQuest Public Health, AustHealth Informit, SCOPUS and Google Scholar to identify 537 citations, 8 of which are included in this review. This review was registered on PROSPERO and a. narrative synthesis is presented following PRISMA guidance. RESULTS: All studies were observational and assessed at high risk of bias. Higher first trimester UV was associated with and improved fetal growth and increased hypertension in pregnancy. Interpretation is limited by study design and quality. Meta-analysis was precluded by the variety of outcomes and methods. DISCUSSION: The low number of studies and risk of bias limit the validity of any conclusions. Environmental health methodological issues are discussed with consideration given to design and analytical improvements to further address this reproductive environmental health question. CONCLUSIONS: The evidence for UV having benefits for pregnancy hypertension and fetal growth is limited by the methodological approaches utilized. Future epidemiological efforts should focus on improving the methods of modeling and linking widely available environmental data to reproductive health outcomes.

Living in stressful neighbourhoods during pregnancy: an observational study of crime rates and birth outcomes.

Background: Patterns of adverse birth outcomes vary spatially and there is evidence that this may relate to features of the physical environment such as air pollution. However, other social characteristics of the environment such as levels of crime are relatively understudied. This study examines the association between crime rates and birth weight and prematurity. Methods: Maternity inpatient data recorded at birth, including residential postcode, was linked to a representative 5% sample of Scottish Census data and small area crime rates from Scottish Police forces. Coefficients associated with crime were reported from crude and confounder adjusted models predicting low birth weight (< 2500 g), mean birthweight, small for gestational age and prematurity for all singleton live births. Results: Total crime rates were associated with strong and significant reductions in mean birth weight and increases in the risks of both a small for gestational age baby and premature birth. These effects, with the exception of prematurity, were robust to adjustment for individual characteristics including smoking, ethnicity and other socio-economic variables as well as area based confounders including air pollution. Mean birth weight was robust to additional adjustment for neighbourhood income deprivation. Conclusion: The level of crime in a mother's area of residence, which may be a proxy for the degree of threat felt and therefore stress experienced, appears to be an important determinant of the risk of adverse birth outcomes.

'Fundamental causes' of inequalities in mortality: an empirical test of the theory in 20 European populations.

The 'fundamental causes' theory stipulates that when new opportunities for lowering mortality arise, higher socioeconomic groups will benefit more because of their greater material and non-material resources. We tested this theory using harmonised mortality data by educational level for 22 causes of death and 20 European populations from the period 1980-2010. Across all causes and populations, mortality on average declined by 2.49 per cent (95%CI: 2.04-2.92), 1.83% (1.37-2.30) and 1.34% (0.89-1.78) per annum among the high, mid and low educated, respectively. In 69 per cent of cases of declining mortality, mortality declined faster among the high than among the low educated. However, when mortality increased, less increase among the high educated was found in only 46 per cent of cases. Faster mortality decline among the high educated was more manifest for causes of death amenable to intervention than for non-amenable causes. The difference in mortality decline between education groups was not larger when income inequalities were greater. While our results provide support for the fundamental causes theory, our results suggest that other mechanisms than the theory implies also play a role.

The role of administrative data in the big data revolution in social science research.

The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data.

Place of work and residential exposure to ambient air pollution and birth outcomes in Scotland, using geographically fine pollution climate mapping estimates.

OBJECTIVES: A relationship between ambient air pollution and adverse birth outcomes has been found in a large number of studies that have mainly used a nearest monitor methodology. Recent research has suggested that the effect size may have been underestimated in these studies. This paper examines associations between birth outcomes and ambient levels of residential and workplace sulphur dioxide, particulates and Nitrogen Dioxide estimated using an alternative method - pollution climate mapping. METHODS: Risk of low birthweight and mean birthweight (for n=21,843 term births) and risk of preterm birth (for n=23,086 births) were modelled against small area annual mean ambient air pollution concentrations at work and residence location adjusting for potential confounding factors for singleton live births (1994-2008) across Scotland. RESULTS: Odds ratios of low birthweight of 1.02 (95% CI, 1.01-1.03) and 1.07 (95% CI, 1.01-1.12) with concentration increases of 1 µg/m(3) for NO2 and PM10 respectively. Raised but insignificant risks of very preterm birth were found with PM10 (relative risk ratio=1.08; 95% CI, 1.00 to 1.17 per 1 µg/m(3)) and NO2 (relative risk ratio=1.01; 95% CI, 1.00 to 1.03 per 1 µg/m(3)). An inverse association between mean birthweight and mean annual NO2(-1.24 g; 95% CI, -2.02 to -0.46 per 1 µg/m(3)) and PM10 (-5.67 g; 95% CI, -9.47 to -1.87 per 1 µg/m(3)). SO2 showed no significant associations. CONCLUSIONS: This study highlights the association between air pollution exposure and reduced newborn size at birth. Together with other recent work it also suggests that exposure estimation based on the nearest monitor method may have led to an under-estimation of the effect size of pollutants on birth outcomes.

'A confession of ignorance': deaths from old age and deciphering cause-of-death statistics in Scotland, 1855-1949.

A large amount of the research undertaken in an attempt to discover the reasons underlying the late nineteenth- and early twentieth-century mortality decline in Britain has relied on the statistics published by the Registrars General. The processes by which individual causes of death are recorded and then processed in order to create the statistics are not, however, well understood. In this article, the authors build on previous work to piece together a time series of causes of death for Scotland, which removes many of the discontinuities encountered in the published statistics that result from the Registrar General deciding to update the nosology, or classification system, which was being used to compile his figures. Having regrouped individual causes of death to 'smooth' the time series, the authors use the new groups to examine the changing causes of death in Scotland for selected age groups, before turning to undertake a detailed examination of mortality amongst those aged 55 or more. The authors find that when deaths from 'old age' in the latter age group are separated from other 'ill-defined' causes, it becomes obvious that there was a 'rebranding' of cause of death. The authors then use individual-level data from two Scottish communities to further dissect the roles played by 'informants' and 'doctors' in this rebranding, in order to see how these roles may have altered over time and what the consequences might be for one's view of how mortality changed in Scotland between 1855 and 1949. Finally, the authors argue that their findings have important implications for some of historical demography's most prominent theories: the McKeown thesis and the theory of epidemiological transition.