National Statistical Office Research Articles

The use of web-scraped data to analyse the dynamics of clothing and footwear prices

Web scraping is a technique that makes it possible to obtain information from websites automatically. As online shopping grows in popularity, it became an abundant source of information on the prices of goods sold by retailers. The use of scraped data usually allows, in addition to a significant reduction of costs of price research, the improvement of the precision of inflation estimates and real-time tracking. For this reason, web scraping is a popular research tool both for statistical centers (Eurostat, British Office of National Statistics, Belgian Statbel) and universities (e.g. the Billion Prices Project conducted at Massachusetts Institute of Technology). However, the use of scraped data to calculate inflation brings about many challenges at the stage of their collection, processing, and aggregation. The aim of the study is to compare various methods of calculating price indices of clothing and footwear on the basis of scraped data. Using data from one of the largest online stores selling clothing and footwear for the period of February 2018–November 2019, the author compared the results of the Jevons chain index, the GEKS-J index and the GEKS-J expanding and updating window methods. As a result of the calculations, a high chain index drift was confirmed, and very similar results were found using the extension methods and the updated calculation window (excluding the FBEW method).

Understanding the Current Geographic Disposition of the Jewish Population of England and Wales: Growth and Contraction

The release in March/April 2023 of England and Wales 2021 Census complete data on “usual residents” by the Office for National Statistics provides an opportunity to analyze, understand and comment on the current geographic disposition of Anglo–Jewry. The analysis presented in this paper incorporates data from the 2001 and 2011 censuses, and makes use of a geodemographic assessment of Jewish communities developed from the 2011 census, setting the scene for changes which have taken place, particularly in the last 10 years. Estimates of the scale of births, deaths and net migration in the 2011–2021 period have been developed to explain why the changes in population have taken place. The potential impact of the coronavirus disease 2019 (COVID-19) pandemic on the census results is also considered. A total of 26 sub-communities in the London and Manchester areas, together with 34 free-standing communities, each with more than 200 Jewish residents, have been analyzed in detail. Unexpected changes in Stamford Hill, Gateshead and Bristol are investigated. A total of 42 smaller communities (60–200 members) are also identified. The paper shows that an understanding of the socio-economic characteristic of each of the communities explains their changes in population since 2011, particularly when factors such as “meta-suburbanisation” in the London fringe area, the impact of student numbers in university towns, and special factors affecting Haredi areas are also taken into account. The picture presented is one of a stable (indeed slightly growing) overall population, but with a large variation in fortunes of the many communities which make up Anglo–Jewry.

The effect of the COVID-19 pandemic on the treated incidence of psychotic disorders in South London

Evidence on the impact of the COVID-19 pandemic on psychotic disorders is so far scarce. We conducted an incidence study to ascertain rates of first-episode psychosis (FEP) before and during the COVID-19 pandemic in South London.We screened clinical records of individuals living in the London boroughs of Southwark and Lambeth who were referred to the early intervention services before (from 1/3/2019 to 28/2/2020) and during (from 1/3/2020 to 28/2/2021) the COVID-19 pandemic. We used the Office for National Statistics to determine the population at risk. We computed crude and sex-age standardised FEP incidence per 100,000 person-years. We used Poisson regression to calculate the incidence rate ratio (IRR) across the COVID-19 pandemic.A total of 321 incident cases of FEP were identified during the COVID-19 pandemic, accounting for a crude rate of 69.8 (95% CI 62.1–77.4) per 100,000 person-years. The crude rate for the year before was 47.5 (95% CI 41.2–53.8). The incidence variation between the two years accounted for an adjusted IRR of 1.45 (95% CI 1.22–1.72). The pandemic was accompanied by a 45% spike in the rates of first-episode psychosis. This finding should inform public health research and demonstrate the need for adequate resources for secondary care.

The Impact of Oral Antibiotics Prior to Cancer Diagnosis on Overall Patient Survival: Findings from an English Population-Based Cohort Study.

There is limited evidence in humans as to whether antibiotics impact the effectiveness of cancer treatments. Rodent studies have shown that disruption in gut microbiota due to antibiotics decreases cancer therapy effectiveness. We evaluated the associations between the antibiotic treatment of different time periods before cancer diagnoses and long-term mortality. Using the Clinical Practice Research Datalink GOLD, linked to the Cancer Registry's and the Office for National Statistics' mortality records, we delineated a study cohort that involved cancer patients who were prescribed antibiotics 0-3 months; 3-24 months; or more than 24 months before cancer diagnosis. Patients' exposure to antibiotics was compared according to the recency of prescriptions and time-to-event (all-cause mortality) by applying Cox models. 111,260 cancer patients from England were included in the analysis. Compared with antibiotic prescriptions that were issued in the past, patients who had been prescribed antibiotics shortly before cancer diagnosis presented an increased hazard ratio (HR) for mortality. For leukaemia, the HR in the Cancer Registry was 1.32 (95% CI 1.16-1.51), for lymphoma it was 1.22 (1.08-1.36), for melanoma it was 1.28 (1.10-1.49), and for myeloma it was 1.19 (1.04-1.36). Increased HRs were observed for cancer of the uterus, bladder, and breast and ovarian and colorectal cancer. Antibiotics that had been issued within the three months prior to cancer diagnosis may reduce the effectiveness of chemotherapy and immunotherapy. Judicious antibiotic prescribing is needed among cancer patients.

Free Time in Old Age: Does Health Status Determine the Choice of Leisure Activities in Indonesia?

This paper examines which leisure activities appear more attractive to older people, considering health status, disability and demographic-socio-economic and environmental variables. It employed data from Survei Penduduk Antar Sensus (SUPAS), the nationwide survey, conducted in 2015 by Indonesia's national statistical office. The sample selected 228,718 individuals aged 60 years old and above. Leisure activities were differentiated between active, sedentary, and no leisure activities. Health is measured with both health status and disability. Disability measures were adopted from Washington Group Short Set on Functioning-Enhanced (WG-SS Enhanced). Our multinomial regression models confirmed that health status was correlated with the choice of leisure activities in old age. Compared to older persons in good health, those reported in poor health without disruption to daily life were more likely to participate in sedentary or active leisure, but those with disruption to daily life were less likely to do so. Older persons with disability were disadvantaged in participating in either passive or active leisure. However, among all selected variables, employment and source of finance had the highest odds ratios and confounded the relationship between health and leisure activity.

Georgia’s Strategic Path: Economic Integration as a Strategic Pathway

Abstract Since achieving independence from the Soviet Union in 1991, the Republic of Georgia has balanced its aspirations to Westernize with its pre-existing and continuing bonds with Russia. Much of the research looking at this dynamic has focused on security and political issues; this article instead focuses on the economic sphere and examines whether Georgia’s economic ties with Europe and Russia have created closer strategic relations. To determine if Georgia is pursuing economic integration with the European Union (EU) and Russia, the article draws on economic data from the National Statistics Office of Georgia on exports, imports, foreign direct investment (FDI), and tourism. It examines the academic literature on Georgia’s strategy of political integration with the EU and its policy of “normalization” with Russia since 2012. Trade and investment have increased with Europe, while tourism from Russia increased substantially between 2015 and 2019. Still, the article finds that Georgia’s economic relations have not created strategic integration with either polity.

Methodological developments in administrative data linkage for cross cutting policy relevant research: Working towards a sustainable data pipeline

ObjectivesDevelop administrative linkages within a national Trusted Research Environment (TRE) that hosts Longitudinal Population Study (LPS) data for over 20 LPS. We will describe the methodological development carried out to enable linkage to administrative datasets. These linked administrative data will support research for public good, informing policy and practice.
 MethodsThe first sets of administrative data under consideration in this Feasibility Study are from the Department of Work and Pensions (DWP), the Department for Education (DfE) and HM Revenue and Customs (HMRC). Working with UK Government departments through a Task & Finish group we have gathered input from DWP, HMRC and DfE and Office for National Statistics (ONS) data sharing experts. The Task & Finish group identified three pragmatic data linkage and data sharing models, that would enable data to be linked via a newly designed secure data pipeline in a legal, secure, and trustworthy manner for all stakeholders.
 ResultsTo encourage sustainability and acceptability, a model designed to be maintained over a long period is based on the re-use of Departmental Personal Identifiable Information (PII) – i.e., name, date of birth, gender, National Insurance number - and attribute data already deposited by the Departments into ONS. ONS will develop for the linkage and extraction of ONS Data into the TRE a system which conducts, and quality assesses the linkage; minimises the Departmental data to participants within the TRE only and the variables specified in the agreements; and, de-identifies the data to their DEA processing standards. The minimised and functionally anonymous data extract will be securely transferred for ingest and integration into the TRE enabling researchers to address a wider range of questions for public benefit.
 ConclusionThis is a model for efficient and low-burden linkages to inform cross cutting research. It will form part of a responsive UK data science capability which can inform government research needs and be used to meet future crisis e.g. new pandemics, the impacts of climate change or economic shocks.

Validation of a dynamic method of measuring households and populations from primary care Electronic Health Records: Cross-sectional comparison with Office for National Statistics Census 2021 estimates

ObjectivesWe have developed a dynamic method for identifying household members from Electronic Health Records (EHR). We compared the 2021 Census estimates of household number and demography with similar estimates derived from primary care EHRs on the Census date using primary care EHRs for the population of north east London (NEL).
 MethodWe included 2,115,017 patients registered with a general practitioner on the 2021 Census date in NEL and assigned households from encrypted Unique Property Reference Numbers. We compared household number and size by Local Authority (LA), Middle Layer Super Output Area (MSOA) and area’s Index of Multiple Deprivation quintiles (IMDq) to Office for National Statistics (ONS) 2021 Census estimates and by LA to ONS Admin Based Housing Stock (ABHS) 2020 estimates. We assessed differences in EHR and Census 2021 populations by sex, age, LA, MSOA and IMDq. Sensitivity analyses will exclude those without a recent recorded clinical encounter.
 ResultsEHR population estimates (2,115,017) were 116,346 (5.8%) higher than Census estimates (1,998,671), higher among men (9.2%) than women (2.5%) in almost all age groups, especially men aged 30-50 years and higher in the most (8.7%), than in the least (2.5%) deprived IMDq. EHR household estimates (660,789) were 68,047 (9.3%) lower than Census estimates (728,836), and 19,719 (3.1%) higher than ABHS occupied addresses (641,070). EHR household size estimates were 15.6%, 29.2%,12.5% and 8.4% lower for household sizes 1,2 3 and 4, and 13.3%, 42.1%, 82.1% and 195.8% higher for household sizes 5, 6,7 and 8 respectively when compared to Census estimates. EHR population and household estimates were respectively 5-10% higher and 5-11% lower for almost all NEL local authorities.
 ConclusionEHR- and Census-derived population and household estimates differ, mainly in the prevalence of larger households. While data were extracted on the same date, person-level validation was not possible. Differences may reflect deregistration delay in EHR when changing residence. Analyses based on clinical encounters recency may identify registered patients who are no longer residents.

Turning data into research-ready data

ObjectivesGovernments acquire extensive data holdings and face increasing pressure to make these available as record-level microdata for research. However, turning data into research-ready data (RRD) is not a straightforward exercise. We demonstrate how even in simple cases researcher involvement can bring substantial rewards for effective RRD development.
 MethodsThis paper reports on an ADRUK-funded project to take a dataset originally collected by the Office for National Statistics for official statistics (the UK Annual Survey of Hours and Earnings, ASHE), formally review its microanalytical characteristics, link it to Census 2011 data, and prepare a new ‘research ready dataset’ with appropriate documentation and coding. This should have been straightforward as the datasets had already been widely used as research microdata. However, the involvement of academic researchers in the production of research-ready data led to many important new insights.
 ResultsThe research programme had 3 aims: testing assumptions about the data; reviewing data quality; and adding value.
 Because of its sampling model, ASHE is assumed to have random non-response both longitudinally and in cross section. The research team showed that was untrue: there was higher attrition than expected, and both longitudinal and cross-sectional non-response appeared non-random..
 The data quality review showed further concerns about the accuracy of some geographical indicators, and some variables of opaque provenance; in contrast, we confirmed the accuracy of administrative variables created by ONS.
 As well as being important for researchers, these findings have the potential for significant effects on official statistics produced from the source data, enhancing the value of the source data.
 Finally, value was added from new variables which reflected the team’s wide research interests
 ConclusionOften in government the assumption is that creating RRDs is a matter of creatign files and giving access to the researchers. Insights from our work show that the deep involvement of the research community can bring rewards for both data holders and researchers. For RRDs, researcher-led construction is vital.

The UK Longitudinal Linkage Collaboration: A trusted research environment for the longitudinal research community

ObjectivesOur Trusted Research Environment (TRE) provides a centralised infrastructure to pool Longitudinal Population Studies’ (LPS) data and systematically link participants’ routine health, administrative and environmental records. All data are held in a centralised research resource which is now certified by UK Statistics Authority as meeting the Digital Economy Act standard.
 ApproachWe have created an unprecedented infrastructure integrating data from interdisciplinary and pan-UK LPS linked to participants’ NHS England records with delegated access responsibilities. Integrated and curated data are made available for pooled analysis within a functionally anonymous DEA and ISO 27001 accredited TRE. We developed a bespoke governance and data curation framework with LPS data managers and Public/participant contributors. New data pipelines are being built with partners at ADRUK and the Office of National Statistics to link non-health records. Our design supports long-term sustainability, linkage accuracy and the ability to link data at both an individual and household level.
 ResultsThis organisation is a collaboration of >24 LPS with ~280,000 participants. Participants' data are linked to NHS records and geo-coded environmental exposures. This resource is now accessible for public benefit research for bona fide UK researchers. Administrative data including tax, work and pensions, and education are being added to the resource. This data flow is enabled by: (1) a model where TTP processes participant identifiers for many different data owners; (2) creation of a novel longitudinal data pipeline, enabling linkage, data extraction and update of records over time; (3) an access framework where Linked Data Access Panel considers applications on behalf of data owners (e.g., the NHS), with review by a Public Panel and distributing applications to LPS for approval of appropriate data use.
 ConclusionOur organisation provides a strategic research-ready platform for longitudinal research. We are extending linkages of LPS participants to previously inaccessible datasets. The research resource is positioned to allow researchers to investigate cross-cutting themes such as understanding health and social inequalities, health-social-environmental interactions, and managing the COVID-19 recovery.

Assessing the economy using faster indicators

AbstractThis paper constructs a coincident indicator for the United Kingdom employing—for the first time—a novel set of “faster” indicators recently published by the Office for National Statistics. Most of these variables were first released during the COVID‐19 pandemic with the aim of facilitating economics and social research by more timely indicators. The empirical evidence suggests that a coincident indicator based on a novel weekly dataset successfully captures the economic conditions in real time. We further use this indicator in an out‐of‐sample macroeconomic nowcasting exercise targeting monthly economic growth, prices and retail sales.

Investigating the effect of inflation on the consumption pattern of Iranian households

The big data sources of National Statistical Offices (NSOs) are provided to make a superior platform for decision-making. The household income and expenditure survey is one of the economically important surveys especially when the inflation rate varies to assess the changes in households’ consumption patterns. In this case, big data can be beneficial and help to accurately measure consumption patterns of urban and rural households at every geographical level. This analysis is an exploratory study for the extraction of the size of injustice and imparity of household income and facilities implemented by classifying and clustering all Iranian households. Through this study, classification and soft clustering (Fuzzy clustering) techniques are employed to characterize the Iranian household types from 2011 to 2021, which are supervised and unsupervised approaches, respectively. Moreover, association rule mining techniques are employed to discover and extract consumption patterns for each cluster. Obtained results showed that there was a significant gap between purchasing power/receiving energy between lowest and highest income households from 2011 to 2021, and this gap is increasing day by day.

The importance of food in studying economic hardship and well-being: Does food insecurity mediate the associations between income and stress and well-being in a UK representative sample?

Abstract Aim Efforts to tackle the global mental health crisis must be underpinned by a robust literature on the social determinants of mental health. Existing studies show consistent effects of economic hardship on mental health, emphasising the importance of basic needs, such as food. Outcomes are affected by family structure, with larger families and households with single adults experiencing greater budgetary strain. Our study aimed to investigate the extent to which effects of income on stress and well-being are mediated by food insecurity, whilst accounting for the effects of family size. Subject and methods We surveyed a nationally representative sample (n = 1004) of UK adults, collecting key demographic information, plus data on food security (Household Food Security Survey Module Six-Item Short Form), perceived stress (four-item Perceived Stress Scale), and well-being (Office for National Statistics’ four-item personal well-being measure). Results Our results demonstrated that meaningful portions of the effects of income on stress (44%) and well-being (37%) can be accounted for by food insecurity. We also found that 42% of the effect of family size on perceived stress could be accounted for by the increased food insecurity experienced by larger families. Conclusion Our results add to a growing body of evidence suggesting that tackling economic hardship and ensuring the satisfaction of material needs would support improved mental health outcomes. Further, given that evidence demonstrates important impacts of stress on other factors such as obesity and cognition, we argue that tackling poverty and ensuring food security would also have physical health benefits.

A Note on the Optimum Allocation of Resources to Follow up Unit Nonrespondents in Probability Surveys

Abstract Common practice to address nonresponse in probability surveys in National Statistical Offices is to follow up every non respondent with a view to lifting response rates. As response rate is an insufficient indicator of data quality, it is argued that one should follow up non respondents with a view to reducing the mean squared error (MSE) of the estimator of the variable of interest. In this article, we propose a method to allocate the nonresponse follow-up resources in such a way as to minimise the MSE under a quasi-randomisation framework. An example to illustrate the method using the 2018/19 Rural Environment and Agricultural Commodities Survey from the Australian Bureau of Statistics is provided.

Influence of socioeconomics and social marketing on smoking in Thailand: A National Survey in 2017.

Smoking is one of the risk factors for noncommunicable diseases and is harmful to both active and passive smokers. This study aimed to identify the influence of socioeconomic and environmental issues on smoking in Thailand. The study is a secondary dataset analysis of cross-sectional data using data from the 2017 Smoking and Drinking Behaviors Survey of the National Statistical Office of Thailand. The survey collected the data among 88689 participants using a structured questionnaire. The multi-level analysis was used to identify the association between socioeconomics, environmental factors, social marketing, and smoking while controlling for the effects of covariates and presenting the adjusted odds ratio (AOR) and its 95% confidence interval (CI). Among 88689 respondents, the prevalence of smoking was 18.2% (95% CI: 18.00-18.51). Factors that were associated with smoking were: exposure to secondhand smoke in residential settings (AOR=15.31; 95% CI: 14.47-16.20) and alcohol regular drinking (AOR=4.44; 95% CI: 4.14-4.76). In addition, social marketing factors include: disagreeing or being unsure of the opinions that cigarettes should be categorized as harmful goods (AOR=3.15; 95% CI: 2.94-3.37); not having been exposed to the disadvantages of smoking in social media (AOR=1.51; 95% CI: 1.43-1.61); not having been exposed to the disadvantages of smoking in newspapers, television, radio, advertisements, or other sources (AOR=1.46; 95% CI: 1.37-1.62); having never seen the warning cautions or having seen them but ignored the hazardous effect (AOR=4.81; 95% CI: 4.5-4.9); and having ever seen the warning cautions/ever seen but ignore the hazardous effect (AOR=4.81; 95% CI: 4.54-5.09), and ever seen advertisements or billboards which motivate smoking in various places (AOR=1.33; 95% CI: 1.24-1.42). Smoking and secondhand smoke are crucial problems that affect health. In addition, related sectors should help to develop a policy recommendation to reduce the smoking rate through social marketing. Strict and comprehensive policies and laws on non-smoking in work places, public spaces, and homes, will help to reduce secondhand smoke exposure among non-smokers.

Spatial association and modelling of under-5 mortality in Thailand, 2020.

Under-5 mortality rate (U5MR) is a key indicator of child health and overall development. In Thailand, despite significant steps made in child health, disparities in U5MR persist across different provinces. We examined various socio-economic variables, health service availability and environmental factors impacting U5MR in Thailand to model their influences through spatial analysis. Global and Local Moran's I statistics for spatial autocorrelation of U5MR and its related factors were used on secondary data from the Ministry of Public Health, National Centers for Environmental Information, National Statistical Office, and the Office of the National Economic and Social Development Council in Thailand. The relationships between U5MR and these factors were modelled using ordinary least squares (OLS) estimation, spatial lag model (SLM) and spatial error model (SEM). There were significant spatial disparities in U5MR across Thailand. Factors such as low birth weight, unemployment rate, and proportion of land use for agricultural purposes exhibited significant positive spatial autocorrelation, directly influencing U5MR, while average years of education, community organizations, number of beds for inpatients per 1,000 population, and exclusive breastfeeding practices acted as protective factors against U5MR (R2 of SEM = 0.588).The findings underscore the need for comprehensive, multi-sectoral strategies to address the U5MR disparities in Thailand. Policy interventions should consider improving socioeconomic conditions, healthcare quality, health accessibility, and environmental health in high U5M areas. Overall, this study provides valuable insights into the spatial distribution of U5MR and its associated factors, which highlights the need for tailored and localized health policies and interventions.

Smart water metering as a non-invasive tool to infer dwelling type and occupancy – Implications for the collection of neighbourhood-level housing and tourism statistics

The international rollout of advanced metering infrastructure (AMI) in the residential water supply sector affords tremendous benefits in driving water-use efficiencies, accurate billing and network management (e.g. leak detection). AMI, using ‘smart meters’ fitted at a dwelling level, record water consumption at high temporal resolution. Since water is typically only consumed when householders are present, these data could offer a non-intrusive means of inferring dwelling occupancy patterns. These insights could have a range of benefits dependent upon the spatiotemporal scale and the intended application – our interest is in the potential of these data to identify dwelling type, specifically to identify dwellings that have occupancy patterns associated with tourism, such as second homes or short-term holiday rentals. We focus on these data in a UK context and draw on data rarely available for academic research. Our data relate to a sample of dwellings in Devon and Cornwall, South West England. They capture high-temporal resolution water consumption during Covid-19 ‘lockdown’ and ‘staycation’ periods, providing a unique opportunity to demonstrate that these data can reveal the unusually pronounced property-level occupancy trends evident during this period. We apply Non-Intrusive Occupancy Monitoring (NIOM) to extract dwelling-level occupancy status (occupied/unoccupied) on a day-by-day basis. We group properties according to their occupancy trends, inferring a set of properties that exhibit occupancy characteristics associated with tourism. We demonstrate that these show correspondence with underlying indicators of tourism activity, drawn from AirDNA records of short-term tourist rental properties in this area. Ongoing global rollout of AMI means that these data will be routinely available at the dwelling level and we reflect on the benefits they could provide in generating near real time insights into dwelling occupancy. Drawing on our collaboration with the Office for National Statistics (the UKs national statistical institute) we outline the considerable potential that these data and approaches could offer in the collation of small area housing and tourism statistics.

Infrastructuring exit migration: Social hope and migration decision-making in EU families who left the UK after the 2016 EU referendum

Since the 2016 EU referendum, estimates on net-migration by the UK’s Office for National Statistics have shown two parallel trends: declining new arrivals from the EU (EU immigration) and increasing departure of EU nationals formerly living in the UK (EU emigration). To date, little is known of the latter and of the circumstances and factors that inform and shape EU citizens’ decisions to leave the UK. Drawing on in-depth qualitative interviews with 37 EU families who left the UK after the EU referendum, this article offers insights into their social hopes, migratory trajectories, motivations and decision-making. Using a family-centred approach, the analysis of these ‘exit trajectories’ through the lens of migration infrastructures reveals a range of challenges EU migrants must negotiate and overcome – often within their households. The analysis complicates assumptions of the meaning and experience of ‘going home’ as seen from a family perspective and reveals the intergenerational tensions, challenges and accommodations that ‘return’ produces and how these differently affect each family member. Faced with diverging interests, needs and expectations, families pursued two main strategies for accommodating these differences: a spatial strategy, namely negotiating and choosing a destination that would suit the present and future of the family members, or a temporal one, planning the exit strategy not as a one-off event but taking place over a longer period. However, accommodation and reconciliation are not always possible, leading in some cases to the fragmentation or dissolution of the family unit.

The Effectiveness, Accessibility, and Feasibility of Price Policy Mechanism in India: Evidence from the Situation Assessment Survey 2018–2019

India’s agricultural price policy came under serious criticism in the past for its inefficiencies and the financial burden that it puts on the state exchequer. Using the household-level Situation Assessment Survey, published by the National Statistical Office (NSO), this study inspects the effectiveness, accessibility, and feasibility of price policy mechanisms in India. It is found that farmers’ income in India has been increasing at a slower rate and the share of income from crop cultivation has been declining over the years. However, farmers who have received the benefits of minimum support price (MSP) and procurement mechanism, reported having a better income as compared to farmers who sell to the local market right after harvest. Indian agriculture is dominated by small and marginal farmers operating on less than two hectares of land. They are more vulnerable to shocks, such as climate change, price crashes, and post-harvest loss. This study critically evaluates the operation of price policy with special reference to the landholding classes of the farmers. It finds that MSP played an important role in keeping the market prices high and ensuring a better return. However, the reach of MSP has been limited to a few crops and in selected states.

Temporal analysis of non-ST segment elevation-acute coronary syndrome (NSTEACS) outcomes in ‘young’ patients under the age of fifty: A nationwide cohort study

BackgroundThe characteristics and risk factor profile of young patients presenting with non-ST segment elevation acute coronary syndrome (NSTEACS) and how they may have changed over time is not well reported. MethodsWe identified 26,708 NSTEACS patients aged under 50 presenting to United Kingdom (UK) hospitals between 2010 and 2017 from Myocardial Ischaemia National Audit Project (MINAP). We calculated incidence of NSTEACS per 100,000 UK population, using Office of National Statistics (ONS) population estimates, prevalence of comorbidities, ethnicity, and in-hospital mortality. We formed biennial groups to enable comparison, 2010–2011, 2012–2013, 2014–2015 and 2016–2017. ResultsThe incidence of NSTEACS per 100,000 population showed minimal change between 2010 and 2017 (2010: 5.4 per 100,000 and 2017; 4.9 per 100,000). Rates of smoking (2010–11; 58% and 2016–17; 53%), and family history of coronary artery disease (CAD) (2010–11; 51% and 2016–17; 44%) fell, but the proportion of patients from an ethnic minority background (2010–11; 12% and 2016–17; 20%), with diabetes mellitus (DM) (2010–11; 14%, and 2016–17; 18%) and female patients (2010–11; 22% and 2016–17; 24%) increased over the study period. Mortality from NSTEACS remained unchanged (2010–11; 1% and 2016–17; 1%). ConclusionsThe incidence of NSTEACS in patients aged under fifty has not reduced despite reduction in prevalence of risk factors such as smoking hypercholesterolaemia in those admitted to UK hospitals. Despite improved rates of early invasive coronary angiography and percutaneous coronary intervention in ‘young’ NSTEACS patients, in-hospital mortality remains unchanged.