MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
Authors: Sanja Scepanovic
Ivica Obadic
Sagar Joglekar
Laura GIUSTARINI
Cristiano Nattero
Daniele Quercia
Xiao Xiang Zhu
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: public health; population health; medical prescriptions; satellite imagery; satellite data; SDGs
TL;DR: This unique dataset combines prescription data, satellite imagery, and socioeconomic data for studying spatial-temporal health attributes in England.

Abstract: As extreme weather events become more frequent, understanding their impact on human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. This limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a comprehensive understanding of health outcomes. Additionally, obtaining appropriate environmental indices across different geographical levels and timeframes poses a challenge. For the years 2019 (pre-COVID) and 2020 (COVID), we collected spatio-temporal indicators for all Lower Layer Super Output Areas in England. These indicators included: i) 111 sociodemographic features linked to health in existing literature, ii) 43 environmental point features (e.g., greenery and air pollution levels), iii) 4 seasonal composite satellite images each with 11 bands, and iv) prescription prevalence associated with five medical conditions (depression, anxiety, diabetes, hypertension, and asthma), opioids and total prescriptions. We combined these indicators into a single MedSat dataset, the availability of which presents an opportunity for the machine learning community to develop new techniques specific to public health. These techniques would address challenges such as handling large and complex data volumes, performing effective feature engineering on environmental and sociodemographic factors, capturing spatial and temporal dependencies in the models, addressing imbalanced data distributions, developing novel computer vision methods for health modeling based on satellite imagery, ensuring model explainability, and achieving generalization beyond the specific geographical region.

MEDSAT: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
Sanja Séepanović Ivica Obadic233 Sagar Joglekar Laura Giustarini Cristiano Ñattero Daniele Quercial.4 Xiao Xiang Zhu
Nokia Bell Labs Technical University of Munich
*Munich Center for Machine Learning A Centre for Urban Science and Progress, King's College London SWASDI
Intercom
Abstract
As extreme weather events become more frequent, understanding their impact human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. This limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a compre- hensive understanding of health outcomes. Additionally, obtaining appropriate environmental indices across different geographical levels and timeframes poses challenge. For the years 2019 (pre-COVID) and 2020 (COVID), we collected spatio-temporal indicators for all Lower Layer Super Output Areas in England. These indicators included: i) 111 sociodemographic features linked to health in existing literature, ii) 43 environmental point features (e.g., greenery and air pol- lution levels), iii) 4 seasonal composite satellite images each with 11 bands, and iv) prescription prevalence associated with five medical conditions (depression, anxiety, diabetes, hypertension, and asthma), opioids and total prescriptions. We combined these indicators into single MEDSAT dataset, the availability of which presents an opportunity for the machine learning community to develop new tech- niques specific to public health. These techniques would address challenges such handling large and complex data volumes, performing effective feature engineering environmental and sociodemographic factors, capturing spatial and temporal dependencies in the models, addressing imbalanced data distributions, developing novel computer vision methods for health modeling based on satellite imagery, ensuring model explainability, and achieving generalization beyond the specific geographical region.
Introduction
The impact of environmental factors on human health has gained significant attention in recent years, particularly in the face of increasing environmental pressures caused by extreme weather events. Understanding these impacts is crucial for effective public and population health interventions. However, existing studies often face challenges in obtaining appropriate health and environmental data.
tine-grained and comprehensive data on the prevalence of medical conditions is often scarce in public health studies. Traditional approaches rely infrequent surveys cohort studies, such NHANES [23], HSE [46], BRFSS [52], The Swiss National Cohort [60], or the UK Biobank [61]. However,
Shareed first authorship.
'The work was done during the author's A14EO Beyond Fellowship at the Technical University of Munich. 'The work was done prior the author's 's tenure at Intercom.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Figure 1: Structure of MEDSAT dataset (single year): This figure illustrates the four data compo- nents constituting MEDSAT: i) sociodemographic features (111), ii) environmental point features (43), iii) image features (4 seasonal Sentinel-2 composite tiles x 11 bands each), and iv) prescription outcomes (prevalence scores for 7 medical prescription types). The distributions of example variables are shown. Sociodemographic variables are presented as percentages ranging from to 1, while environmental variables have varying ranges (e.g.. NO2: (0-3.1 x 10-3) mol/m², μ 2.43 x 10-6, 2.0 x 10-4). Outcome variables represent yearly prescription quantities per capita and are mostly normally distributed (except opioids and total prescriptions). For instance, diabetes prescriptions range from 0.02 to 104.84 (μ = 38.76, 76,76, a = 16.35). Each Sentinel-2 composite image consists of 11 spectral bands. MEDSAT AT offers two such yearly snapshots, for 2019 and 2020. A comprehensive description can be found in the Appendix.
survey methods suffer from biases related to sampling, non-response, recall, and question wording. Cohort studies, while aiming to mitigate these biases, are limited in size, expensive, time-consuming, and prone to participant dropout. The All of Us Research Program [58] is ambitious initiative recruiting over 1 million participants, but its representativeness and long-term engagement remain to be seen. In summary, existing health outcomes data are often limited in scope, granularity, and subject to various biases. Furthermore, despite the increasing availability of finer-resolution measurements for crucial environmental indicators relevant to public and population health, such as greenery. sun radiation, and air pollution, challenges persist in obtaining comprehensive and suitable indices that cover diverse geographical levels and timeframes. While the Earth Observation (EO) community has made significant efforts in capturing detailed satellite imagery with improved resolutions, frequencies, and accessibility, there remains a gap transforming this vast amount of data into user-friendly indices that can be effectively utilized by non non-technical stakeholders and the wider community unfamiliar with EO methods. Even when institutions provide data, such as air quality data from DEFRA [20] in the UK, individuals interested in compiling various environmental information often need to collect from multiple sources, and there are spatial and temporal limitations to the available data.
In this paper, we present the MEDSAT dataset (Figure 1) consisting of four complementary compo- nents and covering two years (2019 and 2020), specifically designed for studying the effects of the environment on population health in small administrative areas in England. Our approach involves an open-source framework that utilizes National Health Services (NHS) practice-level prescription data to extract medical preseription prevalences at fine spatial (i.e., Lower Layer Super Output Area (LSOA)) and temporal (seasonal) scales for the entire population of 57 million. We derived environmental indicators from satellite products, such as Sentinel-5 and OMI, using Google Earth Engine [30]. Additionally, we created cloud-corrected seasonal composite images for 11 spectral bands of multispectral instrument (MSI) Sentinel-2 images in the WASDI platform [67], covering the whole of England, i.c., 130,279 km2. Our dataset also includes 1 111 socioeconomic indicators

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Table 1: A comparison of MEDSAT with similar datasets, some of which not publicly available.
NHS corresponds to the UK health care system and DHS to the Dutch healthcare system.
dataset
bealth Indicatoris)
Indicator source
Imagery
con.
soc.
spatial
public
SustaiBeach (69)
child asortality
surveys
Landsat street view
village (50km)2)
water quality sanitation
Landscape Aesiheties [39]
civironment scenicness
crow cowdsourinng
Semtinel22
1.0km2 V
COVID-19 19 [82)
COVID-19 cases and desths
W11
X
city
X
Greemery & mortality [7]
munality
Eurosiat
X
V
city
X
Greenery & prescribing [31]
unddepresants prescriptions
DIIS
X
V
municipulity tup to 506km2)
X
Nat. enw preserihing [27]
monality
NHS
X
/
1,50A (up to 186m2)
prescriptions: candiovascular umseepresents
MIDSAA
preseriptions:
cespiratery (asthma)
NHS
Semtine-22
LSOA tup lau2)
metabolic (diabbttss, hypersesioo) mesral edepesssion, amiiety). opioids,
obtained mostly from the UK census. By integrating these diverse datasets for the years 2019 and 2020, we provide researchers with comprehensive resource for studying spatial and temporal health attributes and identifying regional health disparities.
2 Related Work
Environmental conditions such as air or noise pollution are relevant indicators for various health issues like asthma or heart diseases [22]. However, the benefits of using EO data to monitor the impact of environmental conditions on human health are still limited. This limitation stems from the existing datasets in the literature listed in Table 1 that either focus on a narrow set of conditions derived from surveys that might not representative of the entire population or do not provide detailed medical prevalence data fine-grained spatial level. Often these datasets are not publicly available and fail to include environmental and sociodemographic features relevant to health studies. For example, the SustainBench dataset [69] presents the problem of predicting 4 different health indicators derived from surveys based on Landsat satellite imagery and street-view images. The spatial unit of this study corresponds to village or local community covering contain additional environmental and sociodemographic features. By considering a more abstract health indicator, Levering et al. [39] introduced dataset that associates Sentinel-2 images with crowdsourced data for landscape scenicness used as a proxy for human health and well-being. By analyzing fine-grained geographical regions of 1.6 km2 the authors discovered plausible associations between a landscape's 's beauty and its land cover distribution. Targeting a specific condition across the population, Temenos al. [62] assembles dataset that relates COVID-19 cases with point features describing environmental data for urban greenness, air quality, sociodemographic features, and health factors. The authors reveal that temperature and mobility trends are among the most important features for predicting COVID-19 cases. Yet, this dataset is not publicly available, does not contain any imagery and the point features represent entire cities, thus describing very coarse spatial units. Further, the impact of the natural environment on mortality rates and prescriptions for various conditions is investigated in [7, 31, 27]. However, the datasets used in these studies are also not publicly available, include only environmental variables related to greenery, and do not contain any imagery. Moreover, the analyses in [7, 31] are performed on larger spatial units like municipalities and cities.
of =558 km2 and this dataset does not
In comparison these works, our dataset, MEDSAT, enables comprehensive modeling of the popula- tion health on a very-fine-graiedd spatial level as it jointly offers environmental and sociodemographic features as well as EO imagery at LSOA level. Further, it covers prescriptions associated with medical conditions, as well as opioids and total, which allow a detailed understanding of specific condition-relatte factors, and shed light on the overall population health and well-being.
3
The MEDSAT Dataset
The MEDSAT dataset serves comprehensive resource for public and population health studies, encompassing medical prescription quantity per capita as outcomes and wide array of sociodemo- graphic, environmental and image features across 33K LSOAs in England (Figure 1). In this release,
3

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we provide data snapshots for the years 2019 (pre-COVID) and 2020 (COVID). Sociodemographic variables align with the latest UK census from 2021. Figure 2 visualizes examples of variables present in MEDSAT.
Access the code at https://gittub. http://twww..ooo//aaaaa///////// and the dataset at bettp://doo http:////oo//..o...//./////// 14459/2023mm1772222 The dataset is released under the CC BY-SA 4.0 license.
3.1 Sociodemographic Features
Our dataset comprises sociodemographic variables sourced from the latest UK Census in 2021 [1]. These variables encompass essential indicators employed in public and population health research, such as gender (percentage of males), age distribution (percentage within 5-year age groups to 85 and above), deprivation scores (pereentage of deprived households in 1-4 dimensions), self-reported health (percentage reporting health on five-point scale), ethnicity (percentage of individuals with White, Asian, Black, or Mixed backgrounds), and English proficiency (percentage reporting English as their main language). Additional variables indirectly related to health outcomes were incorporated, covering religion, commute means and distance to work, residence and housing, profession, and marital status. Our sociodemographic data do not contain any personally identifiable information because census implements stringent privacy protection measures, including targeted record swapping and cell key perturbation, to ensure confidentiality without compromising aggregated statistics [1].
3.2 Environmental Features
We obtained environmental point features for the MEDSAT dataset using various satellite data products on Google Earth Engine (GEE) [30]. For air quality, used satellite data products such as Sentinel-5P NRTI to derive nitrogen dioxide (NO2) [32], TOMS&OMI for ozone [4], and CAMS for total aerosols and PM2.5 [25]. Greenery variables
derived from Sentinel-2 MSI for NDVI and ERAS-ECMWF product for high/low vegetation greenery indices. Climate variables, including wind components, air temperature, soil temperature, atmospheric pressure, and incoming solar radiation, were obtained from ERAS-ECMWE. All land cover variables were sourced from Google Dynamics World product [11]. Importantly, our open-source code can be easily adapted extract other similar indices, and at different spatial scales (e.g., wards or other countries) and temporal scales (e.g.. monthly or different years). More information can be found in Appendix section D.2.
3.3 Image Features
In addition to the above-described approach for deriving preprocessed environmental point features, in our dataset we also included the spectral bands provided by the Sentinel-2 mission [3]. thus resulting in a more comprehensive set of environmental image features. Concretely, we processed Sentinel-2 images for the years 2019 and 2020 through the WASDI [67] platform by calculating average values for each of the four metcorological seasons [49]. We focused on 11 specific bands (Figure 1) capturing relevant environmental factors, excluding the band B10 that is primarily used for cloud detection and water vapor mapping. To ensure data consistency and quality, we resampled the bands to uniform resolution of 10 m, applied cloud masks to exclude affected pixels, and computed pixel-per-pixel averages over time under cloud-free conditions. The resulting composite images represent the typical environmental characteristics for each season (over 500 GB per year in total),
To explore the potential of the high-resolution Sentinel-2 imagery for modeling population health on a fine- ane-grained spatiotemporal level, we extracted image features per LSOA for each meteorological season with the procedure depicted in Figure 4 in Appendix. Concretely, we used the shapefiles that describe LSOA's geographical location crop the LSOA pixels from seasonal Sentinel-2 image. Next, we extracted basic image features from the cropped LSOA pixels by performing the following aggregations per Sentinel-2 band: mean, stdev, min, max, and median, thus resulting in 55 image features per LSOA per season.
3.4 Prescription Outcomes
Extracting Prescriptions on an LSOA Level from NHS data. The monthly practice-level pre- scribing data in England, provided by the National Health Services (NHS) since July 2010 [47],

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diabetes
anxiety
total
100
70
outcomes/label.
60
prescriptions
60
50
medical
40
30
20
20
10
N-S wind
radiation
0.00016
environmental
1.4
0.00012
features/control variables
8.2
0.00008
0.6
White population
prof. occupation
work from home
0.6
sociodemographic
0.5
0.6
0.5
0.4
0.3
0.2
0.2
Figure Visualization the MEDSAT point features. The example distributions for the year 2020 of three health outcomes (diabetes, anxiety, and total prescriptions), three environmental variables (NO2, morth-south wind component, and incoming thermal radiation), and three sociodemographic variables (percentage of White population, professional occupation, and work-from-hone home). The missing values are highlighted black. Depending on the specific analyses intended, the missing value rate will be constrained by the outcomes, standing 5.7%, and ranging up 15.2% if all the features are to be used. Notably, we possess sociodemographic and image features data for all LSOAs. Please refer to Appendix for the details.
constitutes the foundation of our analysis. It offers anonymized information about monthly prescrip- tions across General Practitioner (GP) practices and patient membership to GP practices on an LSOA level. We parse this data to extract prescribed drugs and summarize the total number of patients per LSOA and compute the fraction of GP practice's patients associated with a specific LSOA. Further details about the NHS prescriptions data and the applied procedure for calculating the number of patients per LSOA can be found in the Appendix, Section D.4.
Associating Prescriptions with Condition. To determine prescriptions related to specific medical conditions, our framework utilizes curated lists of drugs, such the one collated for opioids by previous works [59, 18), or it leverages DrugBank [37] to automatically identify drugs associated with given condition (see the Appendix DrugBank section D.4.3). DrugBank is online database that provides comprehensive information on active pharmacological ingredients (APIs) and their corresponding conditions. Each drug name is associated with one more conditions (i.c., symptoms and diseases), drug categories, and an Anatomical Therapeutic Chemical code assigned by the World Health Organization (WHO) for unique identification purposes. For instance, the drug name Citalopram (https://gs drugbank. com/ drugs DDOUg// is linked to range of diseases, including Depression, Anorexia Nervosa, Generalized Anxiety Disorder, and Post Traumatic Stress Disorder. During our crawl, we obtained data on 9,105 drug names from the website, and by filtering out drug names that were not linked to any drug categories, symptoms, or conditions we were left with 3,013 drug names. Next, generated curated list of drugs associated with specific condition by selecting drugs from DrugBank that were linked to that condition (e.g., we associated Citalopram with depression, anxiety, and the other conditions mentioned above).

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Estimating the Prescription Prevalence. To estimate the number of prescriptions associated with specific condition c, we first matched the condition-specttnn drugs from DrugBank with their British National Formulary (BNF) codes from the NHS prescribing dataset. Next, the number of prescriptions for a specific condition in area a is computed using the following formula:
Ne(a)= Σ No(GP) S(GP.a), GPER
((GP)))(GPP.)))
(I)
Here, (GP) represents the total number of prescriptions per GP for drugs associated with the curated list for the condition, and S(GP, a) denotes the fraction of patients of the GP who reside in the area a. To ensure comparability across areas with varying population densities, we computed the metric of "prescriptions quantity per capita," commonly used in medical studies [19, 18], as follows:
Ne(a)
(a(a)
pat (a)
where Upat (a) corresponds to the total number of patients residing in the area a.
For MEDSAT we calculated medical prescriptions associated with three classes of conditions: i) metabolic (diabetes and hypertension), ii) mental (depression, and anxicty), and iii) respiratory (asthma); as well as opioids prescriptions, (which are predominantly prescribed for pain management, but they do have other applications, and have been associated with a consumption crisis in the UK [53, 56]), and total prescriptions, as proxy for general health and well well-being, We highlight that, for simplicity, we use condition names to refer to related prescriptions, however we cannot know for each individual prescription what was the exact cause for which it was prescribed. E.g., "depression prescriptions" means antidepressants and "anxiety prescriptions" means anxiolyties, regardless of actual use. Co-prescriptions across conditions may arise from this method, as it does not ascertain specific prescription reasons, as such details are absent in the NHS dataset. However, the multitude of studies examining prescriptions [59, 18, 8, 41, 34, 33, 31, 44, 35, 63, 12, 66, 29], akin to approach, attests to its significance as a public health outcome.
Our prescription dataset does not contain any personally identifiable information, as it is derived from publicly available monthly prescription data provided at the level of practices, each serving numerous patients.
4 Results
4.1 Revealing Health Inequalities
In Figure in Appendix, present the healtheare accessibility disparities across regions. First, interestingly, we find a prevailing pattern where the number of registered patients exceeds the census population most areas of the country. This aligns with previous investigations by UK authorities [64]. Second, although the correlation (r = .87, ~ 0) is strong, certain LSOAs exhibit ratios.
disproportionate aatient-to-oopulaae
Additionally, our analysis highlights broader factors contributing to healthcare inequalities. The residual values, representing deviations from the linear fit of the patient to the population numbers, correlate with deprivation levels (r .16, s O for mid-deprived areas; r = .22, RJ 0 for highly- deprived areas), suggesting a greater burden on healthcare access in socioeconomically disadvantaged regions. Moreover, the residual values exhibit a negative correlation (r = -.40, R 0) with the percentage of White population, indicating disparities associated with ethnic backgrounds. For more details on this analysis, please refer to Section E. in Appendix.
4.2 Predicting Prescriptions
To evaluate the plausibility of our dataset for modeling population health, we applied classical geostatistical method called Spatial Lag Model (SLM) [5] as well as trained the LightGBM machine learning model [36] to predict the medical prescriptions based on the point features, including image-derived ones, in our dataset. Both models were applied separately for every condition and combination of the environmental, sociodemographic and image features, to better understand the contribution of different input features in modeling population health.

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Table 6 in Appendix displays the SLM results. Collectively, the input features account for a variance ranging from 38% (for diabetes and total preseriptions) up to 63% (for opioids). Individually. sociodemographic features lead the way, explaining between 31% (total) and 52% (opioids) of the variance. They are followed by environmental point features, which account for variances from 13% (diabetes) to 49% (opioids). Image features, though least impactful, still cover a variance from 4% (diabetes) to 28% (opioids).
While the models such as SLM account for the well-known spatial autocorrelation effects [5] present in spatial analysis and modelling (referring to the process that creates clusters of values), machine learning models, such as LightGBM, require special type of cross- voos--vliiationn that is adapted to account for these effects [55]. A alock-buffered cross-validation is a common approach [38], and is implemented in an R package called blockCV [65]. We employed this package to calculate spatial folds on the input of our LSOA shapefiles. For the details, please refer to Appendix E.2. The results obtained through spatial cross- validation using LightGBMM are detailed in Table 2. In the initial row, it is evident that even fundamental image features exhibit predictive capability, albeit to limited extent. Conversely, the subsequent two rows highlight that environmental and sociodemographic features offer improved explanatory power for the observed variances, outperforming image- based features. Furthermore, these feature categories display varying degrees of importance across different conditions. Environmental attributes notably enhance the predictive accuracy of depression, opioid prescriptions. asthma, and total prescriptions. Conversely, sociodemographic features prove more effective in accurately forecasting prescriptions for other medical conditions. Notably, the integration of both environmental and sociodemographic characteristics becomes pivotal for a holistic model of population health. This is exemplified by the last row, indicating that using both, the environmental and the sociodemographic features results in the best R2 scores for all conditions under consideration. Moreover, for both the SLM and the LightGBM model, we observe a consistent pattern that the prescriptions for the mental conditions are predicted with higher accuracy than the ones for the other conditions. In Appendix Section E.2, we present detailed overview of this experiment's setup and provide a comparison of the LightGBM model with Feed-Forward Neural Network (FNN), which shows that the LightGBM model consistently outperforms the FNN.
The average R2 scores resulting from the 5-fold spatial cross- vols--alidationn of LightGBM. These scores are computed across various prescription types and combinations of dataset features specifically for the year 2020.
Table 2:
metabolic
mental anxiety
respiratory asthuma
Input
dlabetes
hypertension
depression
oplolds
total
Image
0,02 =0.07
0.15 00.12
0.15 +0.15
0.14 00115
0.14 10.13
0.19 10.14
0.07 =0.1
Env.
10.8
10.13
0.42 10.15
0.41 10.13
0.37 10.11
0.52 10.12
0.26 10.1
Soc.
0,26 10.1
0.37 =0.11
0.41 10.15
0.39 12
0.32 13
0.47 10.14
0.22 #0.11
Env. Soc.
0.35 =0.08
0.44 =0.1
0.50 1:
0.48 #0.12
0.43 #0.1
0.6
0.31 #0.1
Uncovering Health Factors Understanding the impact of the environment and sociodemographic conditions on human health has been a focus of many studies [14, 2]. Our dataset offers new perspectives on such studies, as it allows the investigation of these relationships on a fine-grained spatial level, and for many conditions simultaneously. To shed light these perspectives, we apply the SHAP [42] approach on the LightGBM models trained for prescription prediction. In Figure show the 10 most important features estimated by the SHAP algorithm for the models used to predict diabetes and total prescriptions. The left plot shows that the sociodemographic indicators describing the occupation, commute habits, migration, and ethnicity are highly relevant for predicting diabetes prescriptions. Specifically, the model establishes a linkage between lower prescription rates and LSOAs characterized by a substantial prevalence of professional occupations, a high number of people working from home, an active bicycle commuting trend, and a prominent student population. Conversely, increased prescription levels are associated with LSOAs having high proportion of individuals of Asian ethnicity. When it comes to the environmental features, the east- west wind component and PM2.5 appear to be relevant indicators as higher diabetes prescriptions align with LSOAS characterized by an eastward wind pattern and heightened air pollution levels, as indicated by the PM2.5 metric. Additionally, bare soil evaporation and ozone stand out as noteworthy contributors to diabetes prescriptions even though these features exhibit no straightforward linear association with their corresponding SHAP values. On the other hand, in the right plot, we note that the environmental features describing canopy evaporation, NO2, cast- west wind, and thermal radiation rank among the
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diabetes prescriptions
total prescriptions
High
High
prof. occupation
canopy evaporation
work from home
Mixed ethnicity
students moved
NO2
commute bicycle
Feature value
east-west wind
Feature value
east-west wind
thermal radiation
PM2.5
students moved prof. occupation good health born in the UK
Asian ethniilty
good health
bare soil evaporation
bad health
Low
Low
200
SHAP
SHAP
Figure 3: SHAP summary plots for diabetes (left) and total prescriptions (right). The SHAP value for a feature indicates its contribution towards the difference between the prediction for an instance and the average model prediction. These plots reveal the 10 most important features for both conditions and the association between the feature values and their importance. In line with the results shown in Table 2, the sociodemographic indicators appear to be more relevant for modeling diabetes prescriptions than the environmental features while the opposite is observed for the total prescriptions. Although a similar set of features are ranked among the most important for both conditions, some features are particularly relevant for specific conditions, such work from home and Asian ethnicity for diabetes prescriptions and thermal radiation and Mixed ethnicity for the total prescriptions.
top-5 most relevant features for estimating total prescriptions. Concretely, high values for canopy evaporation, NO2, and thermal radiation are negatively correlated with the total prescriptions while east-west wind displays similar association for the diabetes prescriptions. With respect to sociodemographic features, LSOAs characterized by mixed ethnicity are associated with a lower number of total prescriptions compared to the ones where the majority of the people are born in the UK. Moreover, we also see that low prescriptions are again associated with a high percentage of students and professional occupations. Finally, for both conditions, self-assessment of health is linked to lower prescription values. Examples of SHAP values for the other conditions well as dependence plots describing the feature interactions are provided in Sections E.2 in the Appendix.
notice that the positive
Describing Health of Environment Using Visual Concepts. To shed light on the potential benefits of using the Sentinel-2 imagery for modeling population health, we reveal the learned visual features patterns in Figure 4 by visualizing examples of LSOA Sentinel-2 images for which the LightGBM model trained on the simple image features closely approximates the actual opioids prescriptions. The LSOAs were visualized with the band combination (B11, B06 and B01) these bands appeared among the most salient image bands according to the SHAP values for the LightGBM model shown in Appendix, Figure 13. First, we note that although the simple image features do not encode the size of an LSOA, they still enable the LightGBM model to associate higher opioid prescriptions with LSOAS covering larger geographical areas. Larger-area LSOAs rural (because these administrative units are designed to have roughly equal populations), and is known from previous research that opioid consumption higher in rural areas [16]. Equally important, we also note that the LSOAs with high prescription values in the first row are characterized by a stronger presence of blue and pink colors occurring near traffic roads than those LSOAs with low opioid prescriptions in the bottom row. Due to the chosen band combination that displays the aerosols (B01) band in blue, this finding points out that the model can relate increased opioid prescriptions for LSOAs exposed to air pollutants. In conclusion, as observed in Table 2 and Table 6 in the Appendix, the environmental and sociodemographic features explain a higher percentage of the variance for prescription predictions. However, this analysis underscores that even basic image features can offer valuable insights into the intricate task of population health modeling. This suggests that leveraging the deep learning methodologies for processing the Sentinel-2 images at the LSOA level has great potential to improve
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Actual prescriptions: Predicted 56
Actual prescriptions: Predicted 50
Actual prescriptions: 50 Predicted 50
LSOAs with high opioids prescriptions
Actual prescriptions: 7 Predicted:
Actual prescriptions: 8
Actual prescriptions: 14
Predicted 10
Predicted 14
LSOAs low opioids prescriptions
Figure 4: Visualizing LSOA Instances in the (B11, B06, B01) band combination. The short wave infrared band (B11) is shown in red, the near infrarnfre band (B06) is shown in green and the acrosols band (B01) is shown in blue. The LSOAs with high and low opioid prescriptions are shown in the first and the second row, respectively. Remarkably, LSOAs with high opioid prescriptions cover a larger geographical area than those with low prescriptions (notice the higher zoom-in level), and have greater presence of aerosols (band BO1) depicted in blue and purple colors.
prescription predictions by capturing the spatial dependencies inside an LSOA. Moreover, using the recent works in eXplainable Artificial Intelligence (xAI) such as [26] can portray environmental health through rich visual concepts, thus opening possibilities for novel insights about the relevant urban and rural structures influencing population health.
4.3 Temporal Analyses
Using MEDSAT, we analyzed temporal differences in outcome and environmental features between 2019 and 2020. Appendix Figures 15 and 16 illustrate varied distributions for both prescription quantities and environmental features. Notably, in the first COVID year (2020), there was a rise in prescriptions for anxiety and depression (in line with reports that the pandemic presented enormous challenges to mental health services in UK [13]) and diabetes medications in England, while asthma and hypertension prescriptions decreased [13].
The environmental shifts during the initial COVID year are evident from altered air pollutant dis- tributions. Satellite data showed decreased levels of NO2, ozone, and PM2.5 in England for 2020 (as reported erlier in [57]). Land cover changes saw increased built areas, likely due to heightened construction activity in the latter half of the year [24, 21], and reduced trees cover. Furthermore. 2020 witnessed elevated temperatures, solar radiation, and both components of wind compared to 2019.
5 Impact, Limitations, and Perspectives
We introduced MEDSAT, a unique dataset providing a comprehensive view medical prescriptions, average yearly environmental indicators, image features, and sociodemographic factors across Eng- land for 2019 (pre-COVID) and 2020 (COVID). This resource enables a thorough assessment of health status for various conditions and exploration of their relationships with sociodemographic and environmental factors.
MEDSAT has three significant impacts. First, it has the potential to empower the development of novel machine learning (ML) approaches tailored for spatially autollyeaautcorrllttee public health data [45], that can augment still predominant traditional statistical models like spatial linear regression [6] and BYM [9], as recent work indicates for xAI models [40]. MEDSAT enables MI. research with large and complex data, effective feature engineering, capturing temporal dependencies, addressing

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imbalanced data, ensuring interpretability, and achieving generalization across diverse regions. Secondly, MEDSAT can facilitate novel discoveries in public health by revealing influential factors that profoundly affect health outcomes. Through SHAP analyses, we confirmed the established link between diabetes and ethnicity, with higher prevalence among people of Asian descent [15, 28), and the preventive effects of biking and active commuting against diabetes and metabolic conditions [54]. Notably, our data from the initial year of the COVID-19 pandemic highlights the impact of socioeconomic factors. Higher percentages of professional occupations and individuals working from home are associated with lower prevalence of diabetes and total prescriptions, underscoring the influence of deprivation knowledge but also expose less-explored connections between the environment and human health. For instance, our SHAP results demonstrated associations between ozone exposure and mental health prescriptions, as well as hetween total aerosols and metabolic condition prescriptions. Furthermore, a north-sound wind is linked to decrease in both types of prescriptions. Thirdly; MEDSAT enables groundbreaking discoveries in population health, particularly regarding health inequalities. Our preliminary analysis uncovers disparities in health accessibility among different economic and ethnic groups. By examining deprivation dimensions such as income, education, and occupational factors, across prescriptions of different types, we can gain a deeper understanding of their contributions to health disparities.
health outcomes [10, 51, 68, 43]. Our findings not only confirm existing
Although MEDSAT is among the most comprehensive mublicly-availabe public and population health datasets to date, it is not without limitations. First. prescription prevalence may not always reflect the true prevalence of the medical condition itself. That is because disparities in healthcare access, privilege, knowledge, and stigmatization can influence prescription rates for certain conditions among different populations [50, 48, 17]. However, it is crucial to note that despite this limitation, our dataset offers a unique opportunity to disentangle these effects, especially when combined with other types of health outcome indicators. Compared to surveys and population samples, which come with their own set of biases, MEDSAT provides a more comprehensive health outcome perspective. Moreover, our method of estimating prescriptions using probabilistic framework, particularly for the four conditions for which we associated drugs using DrugBank, is imperfect. There exists a possibility that we missed certain drug names, or that medications designed for alternate conditions could potentially be inaccurately included. This limitation of our study arises from our labeling method for prescribed drugs. Drugs are labeled according to associated conditions as sourced from the DrugBank database, without claiming any specific intent behind the prescription from the GP. While we can ascertain that drug likely prescribed for a given condition, it is worth noting that drugs can be associated with multiple conditions, both as per DrugBank, and in prescriptions by a GP. This multi-condition association increases the chances of co-prescriptions in our dataset. However, numerous studies on prescription patterns, from antihypertensive [34], to antidepressants [44] to anxiolytics [35] highlight the importance of prescriptions as a health outcome per se and its significance in the field of public health outcomes. We also emphasize that the initial drug list output by DrugBank can be augmented with human expert knowledge in a mixed-method approach to ensure the most accurate results. Our analyses show that the correlations between prescription prevalence scores derived with the automatic and manual methods range from .94 (for anxiety) to .99 for diabetes (see Appendix section D.4.3). Second, our dataset exclusively covers England, representing single developed country. It is noteworthy that England provides high-quality data on both prescriptions and auxiliary census information, and the methods and insights derived from MEDSAT serve foundation for the development of ML approaches that can subsequently be applied to developing countries once high-quality data becomes available, fostering progress towards tracking SDG about health. Third, there is the risk of stigmatizing certain communities based on our dataset. We believe that listed benefits and opportunities offered by MEDSAT outweigh this risk, and we invite ML and health research communities to employing ethical considerations, fostering inclusivity, and ensuring that the insights gained from MEDSAT are used to enact positive change, promote equity, and reduce health disparities.
Acknowledgments and Disclosure of Funding
Project supported by AI4BO Beyond Fellowship at The International AI for Earth Observation Future Lab at TUM/DLR, RR ESA Network of Resources Initiative, German Federal Ministry for Economic Affairs and Climate Action in the framework of the "national center of excellence 4Frthh" (grant number; 50EE2201C), Munich Center for Machine Learning, Nokia Bell Labs, and European Union's 's
10

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Horizon 2020 research and innovation programme under grant agreement No. 869764, awarded to the GoureenRoutes project.
The authors also thank Sinziana Oncioiu, and Dario Augusto Borges Oliveira for helpful discussions, and Paolo Campanella and Bertrand Robert for their support during the dataset creation.
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Appendix
A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
MEDSAT:
Contents
Dataset Licenses
B Dataset Storage and Maintenance Plans
2
Datasheet
Dataset Details
11
D.1
Sociodemographic Features
11
D.2 Environmental Features
D.3 Image Features
11
D.3.1 Extracting Image Features per LSOA
D.4
Prescription Outcomes
12
D.4.1
Existing Public and Population Health Datasets
12
D.4.2
NHS Medical Prescriptions...
14
D.4.3
DrugBank Network
15
Benchmarks Results
16
E.
Detailed Health Inequalities
16
E.2
Predicting Prescriptions
17
E.2.1
Spatial Lag Model (SLM)
17
E.2.2
Machine Learning Models
18
E.3
Temporal Analyses
20
A Dataset Licenses
The dataset is released under the CC BY-SA 4.0 license.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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B Dataset Storage and Maintenance Plans
The dataset is available and will be maintained on TUMMedia, data sharing service from Technical University Munich. It can be accessed via the following link: https://doi org/10. 14459/ 2023mp1714817. The code for datasets creation and experiments can be accessed on https: //github. com/ htt//sana75a /
C
Datasheet
Motivation
For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.
Understanding the impact of the environment human health is crucial for informing policy- making, promoting human well-being, and implementing timely health interventions, particularly in the context of inereasing climate change-induced extreme weather events. Earth Observation data has emerged as valuable resource for addressing critical challenges related to climate change, food security, and poverty [6, 15]. However, current applications that monitor population health using such data have limitations. Health indicators are often derived from surveys [46] and crowdsourcing [24], which may not be representative of the entire population. Additionally, these applications rely on a narrow set of environmental and sociodemographic indicators, hindering comprehensive understanding of the relevant health factors [5, 17, 13]. Moreover, many of these datasets are not publicly available.
In this study, aim address these challenges by providing publicly available dataset that harnesses the potential of earth observation data to monitor and comprehend the environmental influence health outcomes. Our dataset combines four complementary data sources enable prehensive health modeling across the entire population of England. Specifically, for the years 2019 and 2020, integrated medical prescription data at the practice level from the NHS, environmental features, Sentinel-2 satellite imagery, and sociodemographic indicators obtained from the latest UK census in 2021. These data sources were combined at the level of Lower Layer Super Output Areas (Lower Layer Super Output Area (LSOA)), which represent small administrative units ranging size from kkm² to 18km?. Our objectives are to:
Conduct comprehensive health modeling by relating medical prescriptions to environmental, image, and sociodemographic features a fine-grate fine-grained spatial level.
Identify relevant indicators for population health and uncover regional health disparities.
Understand trends and factors influencing population health over time, including the impact of pandemic years on relevant health factors.
Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?
The dataset was created by researchers from:
Nokia Bell Labs, Cambridge, UK.
Technical University of Munich, Munich, Germany.
Wasdi platform (https://www.waal wttp::/wwwwwwwwww.wd...ooooooo
Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.
The creation of the dataset was partly funded by the:
Nokia Bell Labs,
German Federal Ministry for Economic Affairs and Climate Action in the framework the "national center of excellence ML4Earth" (grant number: 50EE2201C).

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Munich Center for Machine Learning.
AI4EO Beyond Fellowship by The International Al for Earth Observation Future Lab at TUM/DLR,
ESA Network of Resources Initiative, and
European Union's 's Horizon 2020 research and innovation programme under grant agreement No. 869764, awarded to the GoGreenRoutes project.
Composition
What do the instances that comprise the dataset represent (e-g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide description.
For years covered (2019 and 2020), our dataset contains the following two types instances:
LSOA instance: Our dataset covers the entire of England by including instance per every LSOA in England. These instances are represented by point features describing the LSOAs environmental and sociodemographic indicators.
Sentinel-2 tile composite instance: For the benefits of using computer vision to extract rich visual concepts that define environmental health, we provide a set of composite Sentinel-2 satellite image tiles per meteorological season covering the entire of England.
How many instances are there in total (of each type, if appropriate)?
In each year, there are 33755 LSOA instances (one per every LSOA in England) and 4 35 composite tile instances (one set of tiles per every meteorological season).
Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset a sample, then what the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If is not representative of the larger set, please describe why not (e.g., to cover more diverse range of instances, because instances were withheld or unavailable).
Our dataset provides a comprehensive overview of the health landscape in England by incorporating data from LSOAs throughout the country. The prescription outcomes are derived from all prescribed items by the NHS, encompassing all patients within the nation during the specified years. Additionally, the census data covers the entire population. In terms of environmental variables, we computed yearly average scores using either all non-cloudy observations of over 30% of images (c.g., for DynamicWone coverage allows for a thorough investigation of health-related factors national level. However, it essential to acknowledge that the representativeness of our dataset diminishes when examining larger geographical regions due to the absence of data from developing countries, and the inherent specificity present even among developed countries.
substantial sample due to computational constraints. This extensive
What data does each instance consist of? "Raw" data (e.g., unprocessed text or images) or features? either case, please provide a description.
The LSOA instances are represented by feature vectors that contain environmental and sociodemo- graphic indicators aggregated over the geographical area covered by an LSOA. In total, there 43 environmental features describing air quality, greenery, climate, and land-cover distribution. Further, we include 11] sociodemographic variables that represent population counts, age group distribution, gender, ethnicity, religion, marital status, employment status, commuting habits, residence and housing, self-reported health, language skills, deprivation and income (the only non-census variable) levels. On the other hand, the Sentinel-2 tile composite instances contain 11 spectral bands that include aerosols, RGB bands, vegetation edge bands, near-infrared bands and short-wave infrared bands. A detailed overview of the environmental and sociodemographic features and the Sentinel-2

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image bands are provided in Figure in the main manuscript and in the "MedSat Variables.csv" file in the public dataset directory.
Is there a label or target associated with each instance? If so, please provide description.
The target variables in our dataset represent medical prescription prevalences yearly level. We included the following 7 target variables representing the quantity of:
diabetes and hypertension prescriptions associated with metabolic conditions,
depression and anxiety prescriptions associated with mental conditions,
asthma prescriptions associated with respiratory conditions,
opioids prescriptions which are predominantly prescribed for pain management, and total prescriptions proxy for general health and well-being.
Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.
In both years, 5, 163 ISOA instances (~155% of the available instances) have missing feature values. The missing values for environmental features occur because some of the used satellite products do provide these features for every LSOA. For example, there are 1,479 (4%) LSOAs for which the variables related to temperature, snow, or radiation derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) Reanalysis v5 product [37] are missing. We observed that this product does not provide these features for LSOAs located near coastal regions. Next, there are 1,507 (4%) LSOAs for which information about the aerosol optical depth and PM2.5 particles is missing from the Copernicus Atmosphere Monitoring Service (CAMS) product [9]. In the future work, we aim to evaluate different satellite products that can provide the missing values for these features. There are 1,956 (6%) LSOAs with missing outcome data. This absence doesn't 't stem from a lack of prescription data from the NHS. Instead, it arises from discrepancy between the LSOA shapefiles of the patient data used for outcome calculation from 2018 and the shapefiles from the 2021 census. We opted to utilize the most recent 2021 shapefiles. We await forthcoming releases of NHS patient data that align with these shapefiles, allowing us to incorporate the data for the missing LSOAs For the sociodemographic indicators, we found that the net annual income missing for 537 LSOAs as this data was not provided on an LSOA level in the UK census but we derived from the latest 2018 estimates of mean annual household income for Middle layer Super Output Areas (MSOAs) from the Family Resources Survey (and there is a mismatch for small number of LSOAs and MSOAs due to the latest adjustments to the ISOAs boundaries in 2021).
Notably, we possess Census sociodemographic and image features data for all LSOAs. Depending on the specific analyses intended, the maximum missing value rate could be constrained by the outcomes, standing at 5.7%. For instance, this applies if user does not require environmental variables from ECMWF or CAMS products.
Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit.
To combine both types of instances, we provide shapefiles describing the LSOAs coordinates that allow extraction of image features for individual LSOAs from the composite Sentinel-2 satellite image. Based on these shapefiles, in our code repository we provide an implementation for the extraction of LSOA-specific image features out of the Sentinel-2 images. Further, the region column in the spatial data files determines the larger geographical area to which the LSOA belongs and can be used cluster the LSOAs based on their spatial proximity and perform health analysis over larger geographical area.
Are there recommended data splits (e.g., training, veviloomennt/vliiaatinn,, testing)? so. please provide description of these splits, explaining the rationale behind them.
We recommend the spatial data split described Section F.2.2 where ISOASs are first clustered into blocks of sizes 28km 28km. Next, those blocks are randomly assigned to 5 folds, each fold

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containing an equal number of blocks. These procedure ensures that LSOAs belonging to a same block do appear within the training and the test set.
Are there any errors, sources of noise, redundancies in the dataset? If so, please provide a description.
The environmental and image features utilized in our dataset are derived from high-level satellite products, which undergo preprocessing to mitigate common errors associated with remote sensing acquisition. However, is important to note that our subsequent processing and spatial aggregation of this data may introduce sources of noise. For instance, we employ threshold 0.2 for NDVI values to calculate the fraction of greenery pixels per LSOA. Consequently, different threshold values may yield varying results. Additionally, the prescription values are derived through probabilistic associations between patients and nearby LSOAs, well as drug names and corresponding conditions. This process, while valuable, not without imperfections and introduces some level of noise.
In terms of redundancies, our dataset contains many correlated features among the environmental and sociodemographic indicators. subset correlated environmental features presented in Figure where it can be seen that the temperature feature is strongly positively correlated with surface soil temperature, thermal radiation, and atmospheric features while being strongly negatively correlated with snow density. These correlations represent challenge for many machine learning models and we encourage the researches to investigate approaches for feature selection that can lead to more efficient and accurate health modeling.
1.00
temperature
0.75
surface soil temperature
0.50
density
0.00
thermal radiation
0.25
-0.50
atmospheric pressure
-0.75
nuracc soil camperatuu
thermal rediaton
surt
Figure 1: Example correlated environmental features.
Is the dataset self-contained, does it link to otherwise rely external resources (e.g., websites, tweets, other datasets)? it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
The dataset is self-contained.
Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals non-public communications)? If so, please provide description.

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Our four complementary dataset components are derived from public data sources. As such, they are not confidential.
Does the dataset contain data that, viewed directly, might be offensive, insulting, threatening, might otherwise cause anxiety? If so, please describe why.
Our dataset does not contain any data that can directly perceived as offensive or inappropriate. As mentioned in the limitations section, the only potential concern is the possibility of stigmatization at the level of LSOA. However, it worth noting that such stigmatization can also be derived from other publicly available data sources.
Does the dataset relate to people? If not, you may skip the remaining questions in this section.
Yes.
Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.
Our dataset identifies a pereentage of subpopulations by age, ethnicity, and gender across LSOAs derived from the UK census. However, these features do not point to any personally identifiable information because the census implements stringent privacy protection measures, including targeted record swapping and cell key perturbation, to ensure data confidentiality without compromising aggregated statistics [1]. The age group and ethnicity distributions are visualized in Figure 2 which shows that the age groups are balanced except for the age groups over 80 years which occur less frequently compared to the age other groups. When it comes to ethnicity and gender, the White ethnicity dominant in the dataset while the gender distribution is balanced.
Ethnicity distribution
age group distribution
0.08
0.8
percent
percent
0.6
0.04
0.4
0.00
and under
10 15 to 19 years
to 9 years
20 to 24 years
years
30 to 34 years
29 years
35 to 39 years to 44 years
40 45 49 years
to 54 years 50
55 60 65 70 to 74 years
to 59 years
64 years to 69 years
75 to 79 years
80 84 years
85 years and over
Asian Asian ethnicity
Black ethnicity
Mixed ethnicity
White ethnicity
A mearssal
to
29
age group
Ethnicity
Figure 2: Age group and ethnicity distributions
Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.
No, for the reasons stated above.
Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.

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Certainly, we have extracted sensitive information such as ethnicity, religious beliefs, and economic deprivation per LSOA from the census data. However, it important to note that this information publicly available through census records.
Collection Process
How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g.. part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data velifitted/errffiee?? so, please describe how.
The environmental variables were already preprocessed by the satellite products, and we didn" t perform further validation. They have varying frequencies, from daily to monthly, depending on the specific product. In our dataset, we created the environmental features by averaging the variables derived from these products on a yearly level. The Sentinel-2 tiles were derived by aggregating publicly available, cloud-free Sentinel-2 images per calendar season that were already preprocessed by the Sentinel-2 mission. Further, the sociodemographic variables are obtained as single values from the UK census data, and as such, do not require validation. The target variables include yearly aggregates of prescription quantities issued by the NHS health authorities. While there is limited condition prevalence data available at the LSOA level across England, our tests for London at the ward level showed moderate to high correlations between our prescription prevalence scores and condition prevalence, ranging from 0.76 for diabetes 0.89 for depression. However, it is important to note that prescription scores and condition prevalence may not correlate perfectly, as they capture different health constructs.
What mechanisms or procedures were used to collect the data (e.g., hardware appa- ratus or sensor, manual human curation, software program, software API)? How were these mechanisms procedures validated?
our data collection process, we developed our own parser for extracting prescription data. Addi- tionally. for gathering environmental variables, we utilized the Google Earth Engine platform [14] for point data collection and the WASDI platform [44] for image data collection. The code for data collection and preprocessing is available on our GitHub repository.
the dataset a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)
Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?
The data collection process for this study was carried out solely by the co-authors, who were involved in the research part of their respective roles.
Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.
All the data were collected in 2022.
Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
As publicly available data was used, this not needed.
7

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Does the dataset relate to people? If not, you may skip the remaining questions in this section.
No.
Did you collect the data from the individuals question directly, or obtain it via third parties or other sources (e.g., websites)?
NA
Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, otherwise reproduce, the exact language of the notification itself.
NA
Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.
NA
consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide description, as well as a link or other access point to the mechanism (if appropriate).
NA
Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., data protection impact analysis) been conducted? If so, please provide description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.
NA
Preprocesiiggddaannng//aaeiiggg
Was any preprocesiin//ddaainnnnaabiinn of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? so, please provide description. not, you may skip the remainder of the questions in this section.
Yes. The environmental features were averaged on a yearly level, the prescription quantities were normalized according to the number of patients residing in an LSOA, and the Sentinel-2 images were averaged per calendar season.
Was the "raw" data saved in addition to the preproceseet/cdennedlbbeeed data (e.g., to support unanticipated future uses)? so, please provide a link or other access point to the "raw" data.
No, but the used data sources are available publicly from the NHS and ONS wesbites, and GEE and WASDI platforms.
Is the software used to preprocessiclean/lbbel the instances available? If so, please provide link other access point.
Yes, on the provided github repository.
Uses

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Has the dataset been used for any tasks already? If so, please provide a description.
In the main manuseript, have presented the potential value of dataset in the context of prescription prediction, analysis of relevant health factors, and examination of health disparities.
However, there have been no published works utilizing this dataset date.
Is there a repository that links to any or all papers or systems that use the dataset? so, please provide link or other access point. NA.
What (other) tasks could the dataset be used for?
The geographical division within our dataset presents challenge in developing machine learning models that can effectively generalize health findings across diverse regions. Additionally, the inclusion of two years of data in our dataset offers the potential for predicting future health outcomes based on historical health data.
Is there anything about the composition of the dataset the way it was collected and prrrocessee/cleenedllabeeee
that might impact future uses? For example, is there anything that future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide description. Is there anything future user could do to mitigate these undesirable harms?
We have meticulously collected and preprocessed the data in accordance with the highest ethical standards to prevent any misuse of our dataset. We strongly urge fellow researchers to conduct responsible analyses and utilize the dataset with integrity.
Are there tasks for which the dataset should not be used? so, please provide a description.
We caution against presenting the results from our dataset in any stigmatizing way, such as highlight- ing the worst areas based on certain attributes, without a clear purpose intention address the underlying issues. It important to use the data responsibly and consider the potential implications of how the results are presented and interpreted.
Distribution
Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? so, please provide a description.
The dataset will be publicly available.
How will the dataset will be distributed (e.g., tarball on website, API, GitHub) Does the dataset have digital object identifier (DOI)?
As stated in Section B, the dataset is publicly available on TUMMedia through the following DOI: https://doi. org/ /0/./.0..0 10. 1.0.14400 14459/20000 20055/2222mmp11111111
When will the dataset be distributed?
The dataset is publicly available.
Will the dataset be distributed under copyright or other intellectual property (IP) license, and/or under applicable terms use (ToU)? so, please describe this license and/or ToU, and provide link other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.
No.

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Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide link other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
No.
Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.
No.
Maintenance
Who will be supportingghosttingimmaaannnnn the dataset?
TUMMedia will host and maintain the dataset which ensures the long-term accessibility and citability of our dataset, facilitating its use and reference by the research community.
How can the owner/curator/manager of the dataset be contacted (e.g., email ad- dress)?
The email addresses of the authors will be available on the GitHub page.
Is there an erratum? so, please provide a link or other access point.
No.
Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated users (e.g., mailing list, GitHub)?
As part of our ongoing efforts, we are currently developing a prescription parser that enables the calculation of preseription quantities Additionally. we are enhancing the drug matching module by leveraging state-of-the-art Large Language Models (LLMs). Once these tasks are completed, we will be able to update the GitHub with the updated parser so that interested researchers can calculated additional target variables and enhance the temporal granularity of those features.
monthly basis and going up 10 years in the past.
the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? so, please describe these limits and explain how they will be enforced.
No.
Will older versions of the dataset continue to be supportelhosteemmaitttneee If so, please describe how. If not, please describe how its obsolescence will be communicated to users.
No.
others want to extend/augment/bildd on/contribute to the dataset, is there mech- anism for them to do so? If so, please provide a description. Will these contributions be validated/verifiee??? If so, please describe how. If not, why not? is there a process for communicaiingddistibbtiigg these contributions to other users? If so, please provide description.
Yes, the code for collecting the four complementary components of the MEDSAT datasets is publicly available on our GitHub repository: https:// https:///ithbbb cwm/comm//na//c jtmm/aanaas//MdSatt
10

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D Dataset Details
D.1 Sociodemographic Features
All the sociodemographic variables are collected from the UK 2021 Census using https: www..nomiswei co. ht//sources/censt saarres/ceesus_222.... _021,, except for income, which is collected from Family Resources Survey 2018 /t/ wtw..u//gooerrmennoooeeetttoooo
eamily-resources--aee survey--2..
D.2 Environmental Features
For this release of the MEDSAT dataset, we used the sources listed in Table 1 to derive our envi- ronmental features. We performed spatial reduction and averaging of yearly values at the LSOA level. When reducing image data to our areas of interest, we employed specific spatial resolution scaling values: 1000 for Sentinel-5, CAMS, and TOMS&OMI, and 10 for ESA- WorldCover and Sentinel-2. These choices were guided by the original products' resolutions and the processing limitations within the GEE platform.
Table 1: Sources and Statistics about of Environmental Variables, The links below should all be prepended with Satellite/Source
Type
Scale (m)
Resolution (m)
Link to data source
Air Q
Sentinel-S
1000
1113.2
COPERNICUS_S$55 WWWWWICCSSS_P_L_________
LFFFL_L3_MT
Q
TOMS&OMI
1000
111000
TOMS_ TDMS_MERGED
Air
CAMS
5000
44528
ЕCИWF CAMS_ _NRT
greenery
Sentinel-2
50
10
COPERNICUS. _________________
greenery
ERAS-ECMWF ECMWF
100
I1132
ECMWF ERA5 ERAS_LAND LL___AADONDLLLL....... WWN_MOOTTTTLAE AGGR
climate
100
11132
ЕСИWF ERA5 LAND_MONTHLYY
MONTHLY AGGR
land cover
Dynamic World
200
10
GOOGLE_ DYNAMICWORLD. V1
D.3
Image Features
To generate composite images, obtained raw Sentinel-2 satellite data and processed it using the WASDI platform lattps://wwl http://wwww........ wasdi. .nt),, which allows access and online processing both public and commercial datasets. For our analysis, we processed Sentinel-2 images corresponding the years 2019 and 2020 by calculating the average values for each of the four meteorological seasons in the both years, defined by the Met Office [32]. Each Sentinel-2 image consists of 13 bands, with four bands at spatial resolution of 10 six bands at 20 m, and three bands at 60 m. We focused on 11 specific bands, as described in main manuscript, which capture environmental factors relevant to health (c.g., BO1 captures acrosols, representing air quality). We excluded bands BO9 and B 110, as the former is primarily used for cirrus cloud detection and the latter used map water vapor.
To generate composite images for each season, we collected and parsed all images within the corresponding three-month period (on average 1000 images per season). This amounted to parsing around 2TB per season and around 8TB per year (see Table 2). For image processing, we conducted several steps to ensure data consistency and quality. Firstly, resampled the selected bands from their original resolutions of 60 m and 20 m to uniform resolution of 10 m. This resampling allowed for a consistent analysis across all bands. Next, we applied the cloud mask provided with each Sentinel-2 image to each of the 11 bands. This cloud mask effectively identified and excluded pixels affected by cloud cover, ensuring the accuracy and reliability of the data. Then, we computed pixel-per-pixe average over time, considering only values observed in cloud-free conditions. This averaging process ensured that the final composite images represented the typical environmental characteristics for each season. Visualizations of subset of composite bands for the summer of 2020 for LSOAs with the highest and lowest total prescriptions per capita are shown in Figure 3.
D.3.1 Extracting Image Features per LSOA
To extract Per-LSOA features from Sentinel-2 composite images, we segmented each image into LSOAA-peccfiic imagelets with the procedure depicted in Figure For every imagelet, we computed five descriptive statistics: min, max, mean, std, and median. Our examination of these statistics
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highest prescription LSOA
lowest prescription LSOA
optical bands
single band BO6
single band B01
Figure 3: Visualization of the selected MEDSAT Sentinel-2 composite bands for the LSOAs with highest/lowest total prescriptions quantity per capita.
revealed core-ccorelations, leading three distinct classes of correlated bands for each image (e.g.- cross-corre corselcorrlltionn of mean values between summer and winter composite images for 2020 can be seen in Figure 5). Further assessment the statistics' distributions showed skewness in some (like min) and both skewness and extreme outliers in others (specifically max). Moreover, certain statistics, such as mean and median, exhibited inherent correlations.
D.4
Prescription Outcomes
D.4.1 Existing Public and Population Health Datasets
Fine-grained spatial and temporal indices for prevalence of medical conditions are rarely available. Public health agencies typically collect data infrequently through representative surveys (e.g.. National health and nutrition examination survey (NHANES) [11], Health Survey for England (HSE) [29], or Behavioral Risk Factor Surveillance System (BRFSS) [33])
population samples with cohort studies (e.g.. Framingham Heart Study [26]. The Swiss National Cohort (SNC) [38]), or
The UK Biobank [39]). Surveys come with well-known biases, such as sampling [7], non-response
[36], recall [19], or question wording [40] bias. While cohort studies aim at limiting these biases
by complementing participant questionnaire responses with their health records,
genetic
1
2
Me-
Bax Bax
Bey
dgg
Input Sentinel-2 Raster (view from bands B4 03 U2)
Cropped raster to LSDA shape boundaries
Image
festures
Figure 4: Extracting Image Features: For each seasonal Sentinel-2 composite image, we extracted
set of five metrics per LSOA from each band B, E {BoI,... Bn1): mean, stdev, min, max, and median. While we show only 3 representative bands for visualization purposes, features were extracted from all 11 bands, resulting in total of 55 features per seasonal image.
12

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Table 2: The size of the data processed on WASDI platform while producing the environmental image features for each month.
date from
date
GB
2018- 0018-12-01
1013-12-31
67 671.4277637
2019-01-01
2019-01-31
671.1432031
2019-02-01
2019-02-31
652.656716 187
2019-03-01
2019-03-31
648,4059570
2019-04-01
2019-04-31
632,4159082
2019-05-01
2019-05-31
617 2984698
2019-06-01
2019-06-31
641.2775684
2019-07-01
2019-07-31
650.5995117
2019-08-01
2019-08-31
661.7470703
2019-09-01
2019-09-31
631.0063281
2019-10-01
2019-10-31
631.5184570
2019-11-01
2019-11-31
666.4724512
2019-12-01
2019-12-31
688.07 688.0771484
2020-01-01
2020-01-31
657.6159667
2020-02-01
2020-02-31
648. 892259
2020-03-01
2020-03-31
614.7591400
2020-04-01
2020-04-33
626.5712695
2020-05-01
2020-05-31
676.6889844
2020-06-01
2020-06-31
640,9450688
2020-07-01
2020-07-31
648.61 13184
2020-08-01
2020-08-31
647. 647.2279785
2020-09-01
2020-09-31
654.1471207
1020-10-01
2020-10-31
941090
2020-1 11-01
2020-11-31
655.3504297
2020- 12-01
2020-12-31
622.121
2021-01-01
2021-01-31
672.5485547
Creze Demiltti
rear tovee JADDE mon be
Or (UO0 mew
saagg jou
Figure 5: Cross-correlation matrix for mean band values extracted from summer (JJA) and winter (DJF) Sentinel-2 composites for 2020. We that bands B01 B05, and B06 -8.A, as well as B11 B12 form clusters highly correlated features.
information, they are still limited in size, can be expensive and time-consuming to conduct, and participants may drop out of the study. The All US Research Program (AoURP) [34] a unique effort in the US recruiting over million participants to study precision population health. However,
13

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despite its size, AoURP still might face limited representativeness. and the success of the program will depend on long-term engagement and retention of participants. In summary, exhaustive health outcomes data are often of limited scope and granularity, and are prone to a variety of biases.
D.4.2 NHS Medical Prescriptions
The practice-level prescribing data in England has been published monthly by the National Health Services (NHS) Business Services Authority since 1998. This data provides information on the quantity and cost of prescribed medications for each general practitioner (GP) practice in the country, Although freely accessible, it is provided in individual files for each year, and is difficult combine in order to analyze spatial or temporal trends. The OpenPrescribing service [10] enables the exploration of trends items, cost, price per item, and quantity per item for each medical prescription and GP practice from 1998. However, a single GP practice can cater to patients from multiple nearby administrative areas (e.g., LSOAs). Consequently, this service does not directly provide insights into the spatial prevalence of prescriptions across these areas. The PrAna R package [21] allows the calculation of prescribed quantities of active pharmaceutical ingredients (APIs) by postcode. However, a single API or medical drug can be used to treat various conditions (e.g., selective serotonin reuptake inhibitors (SSRIs) are used for treating depression, anxiety disorders, and certain eating disorders), and specific medical condition is typically treated using multiple drugs or APIs (e.g.. depression is treated with both SSRIs and trieyclic antidepressants (TCAs)). This limits the ability of such tool to understand the prevalence of prescriptions for specific conditions. As result, previous studies using prescribing data in England have mainly focused on a small number of drugs manually aggregated for bespoke analyses, primarily examining temporal trends (c.g.. [20, 30. 8, 45, 12]), with a few studies investigating also spatial trends (e.g., [35, 18]. Despite the valuable insights provided by previous studies using prescribing data, the best of our knowledge, there is currently no approach that generalizes across various medical conditions to calculate spatial and temporal trends from this data.
Original NHS Practice-Level Prescribing Data The monthly practice-level prescribing data in England, provided by the National Health Services (NHS) since July 2010 [31], constitutes the foundation of our analysis. The dataset consists of four files visualized in Figure 6 containing the following information:
GP monthly prescriptions - Anonymized prescriptions across General Practitioner (GP) practices in England for given month. Each prescription entry includes details such as the drug name, British National Formulary (BNF) code [31], practice code, total number of items, total cost, and individual item quantities.
2. Drugs A comprehensive list of drugs with their unique BNF codes [31].
3. GPs - Information on GP practices, including practice codes, names, and full addresses. Our dataset encompasses 6,924 GPs located across England, excluding closed or prison-hosted practices.
4. Patients Contains practice codes, census Lower-layer Super Output Area (LSOA) codes, and the number of patients registered with each practice in particular area. By aggregating this data, we calculated the total number of primary care patients residing in a specific area using the equation:
Pepa (a) = Σ nen
Pup (gp, a.)
Here, Mpp a) represents the number of patients registered with particular GP residing in area a. The strong correlation (r = .92) between the number of patients and residents in an LSOA validates our probabilistic approach of assigning patients to areas. Additionally, we computed the fraction of GP practice's 's patients associated with specific area using the equation:
Mpar (gp a)
f(g., a)
(2)
ppar
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Proediee Code
Code
Pils
( NHS GP monthly praccription
BKP Oode:
Deug
Praciies
Name
Pestende
Practiee/Code
LS0A
Petieuts
0105810T6
Ranitidise
NB1013
High Streel
SX11 6t
NB1915
101012198
L0
Sengery
(b) NBS druge
60
GP patiants
Figure 6: The four NHS Datasets: (a) GP (general practitioner) monthly prescriptions; (b) Drugs; (c) GPs; and (d) Patients. Each monthly prescription in dataset (a) was translated into a drug name based on the BNF code for which dataset (b) offered the corresponding drug (preparation name). The prescription was also geographically mapped using the GP code for which dataset (c) provided the location. To then map the prescription at the level census LSOAs, we computed the fraction the GP's patients who lived in each LSOA from dataset (d).
D.4.3 DrugBank Network
We show here the lists of drug names extracted automatically from DrugBank for two conditions, i.e., diabetes (Table 3) and anxiety (Table 5), and the pre-existing list from the literature [16] for depression (Table 4). For anxiety, the other lists of drugs, please refer to our GitHub repository: https://githin com/ sanjaas//////////// tetaat//teeemmmtttt cctrr/coe//cccee mrode//oolle ccollate_daaee data/NNS wwwppprpeiiinnnn...rrrrppppppeep latooo_passrrrrrrre names.
also visualize the corresponding DrugBank network subset (Figure 7). For
The correlation between prevalence scores based on the list of drug names extracted automatically from DrugBank and based on the pre-existing list from the literature are .94 for anxiety and .99 for diabetes, attesting to the high quality of automatically generated output from DrugBank.
Table 3: List of drug names associated with diabetes.
BNF Code
Drug name
BNF Code
Drug name
0601023A0
Acarbose
U601023AM
Canagliflozin
0601023AS
Albiglutide
0601023AP
Canagliitozin/Mettoon
0601023AK
Alogliptin
0601021E0
Chlorpropamide
0601023AJ
Alogliptin/Metfommi
0212000AD
Colesevolam Hydrochloride
0607010B0
Bromocriptine
0601023AG
Dapagliflozin
0601023AL
Dupagliflozin/Metoomm
0605020E0
Desmopressin Acetate
0601023AQ
Dulaglutide
0601023AN
Empagliftozin
0601023AR
Empugliifozin/MMttorn
0601023AX
Ertuglitlozin
0601023Y0
Exenatide
0601021110
Glibenclamide
0601021M0
Gliclazide
0601021A0
Glimepiride
0601021PU
Glipizide
0202010L0
Hydrochlorothiazzde
0202080M0
HbsdrochoorothizziddPoossss
0601011A0
Insulin Aspart
060101220
Insulin Degludec
0601012X0
Insulin Detemir
0601012V0
Insulin Glargine
0601012AB
Insulin Glangine/Lixisenatide
060101 1PO
Insulin Glulisine
0601011R0
Insulin Human
0601011LD
Insulin Lispro
060101250
Isophane Insulin
0601023AE
Linagliptin
0601023AF
Limagliptin/Metfrrmin
0601023AB
Liraglutide
0601023AT
Lixisenatide
0601022B0
Metformin Hydrochloride
0601023W0
Metformin Ilydnochooidde/Pogliice
0601023V0
Metformin (yydrochloride/Rosiiltzaaa
0601023AD
Metformin HlydnchhoorideSSitagiiin
060102320
Metformin Hy Hydrochloride/ Viiddd//iillagliiiinn
0601023M0
Miglitol
060302330
Nateglinide
0601023B0
Pioglitazone Hydrochloride
0601023R0
Repaglinide
060102380
Rosiglitazone
0601023AC
Saxagliptin
0601023AV
Saxagliptin/ Dageiipiin//palllllozzin
0601023AH
Suxigliptin/Mttirmmin
0601023AW
Semaglutide
100330XX0
Sitagliptin
0601021V0
Tolazamide
060102 X
Tolbutamide
0601023AA
Vildagliptin
15

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Table 4: List of drug names associated with depression (source [16)).
BNF Code
Drug name
BNF Code
Drug name
0403030Q0
Sertraline
0403040TO
Rehoxetine
0403030D0
Citalopram
040304020
Agomelatine
0403030E0
Fluoxetine
0402010S0
Flupentixol
0403030PM
Paroxetine
0403010TO
Tryptophan
0403030X0
Escitalopram
0403040NO
Nefazodone
0403030FO
Fluvoxamine
0403010U0
Oxitriptan
0403040M0
Mirtazapine
0403010B0
Amitriptyline
0403040W0
Venlafaxine
0403010V0
Trazodone
0403040Y0
Duloxetine
0403010H0
Dosulepin
0403040AB
Vortioxetine
0403010L0
Lofepramine
1403010RO
Nortriptyline
0403010C0
Clomipramine
040301 OJO
Imipramine
0403010W0
Trimipramine
0403010G0
Doxepin
0403010M0
Mianserin
0403010A0
Amoxapine
0403010PO
Moclobemide
0403010X0
Tranyleypromine
0403010S0
Phenelzine
0403010K0
Isocarboxazid
Table 5: List of drug names associated with anxiety.
BNF Code
Drug name
BNF Code
Drug name
0401020A0
Alprazolam
0401020G0
Bromazepam
0403010B0
Amitriptyline Hydrochloride
0401020BB
Buspirone Hydrochloride
0401020D0
Chlordhazepoxide
0401020E0
Chlordiazepoxide Hydrochloride
0403030D0
Citalopram Hydrobromide
0401020V0
Clorazepate Dipotassium
0401020K0
Diazepam
0704020AA
Duloxetine Hydrochloride
0403030X0
Escitalopram
0408010G0
Gabapentin
0304010J0
Hydroxyzine Hydrochloride
0401020PO
Lorazepam
0401020R0
Meprobamate
0403040X0
Mirtazapine
0401020TO
Oxazepam
(403030PD
Paroxetine Hydrochloride
0402010Q0
Perphenazine
(406000TO
Prochlorperazine Maleate
020400ORO
Propranolol Hydrochloride
0402010AB
Quetiapine
0402010X0
Trifluoperazine
0403040W0
Venlafaxine
E Benchmarks Results
E.1
Detailed Health Inequalities
In Figure 8, we present the MEDSAT data, providing insights into healthcare accessibility disparities across regions. Our analysis focuses on comparing the total number of registered patients with the population at the LSOA level, revealing significant spatial deviations, indicative of inequalities.
First, interestingly, we find a prevailing pattern where the number of registered patients exceeds the census population in most areas of the country. This aligns with previous investigations by UK authorities [42]. Second, although the correlation (r .87, p R 0) is strong, certain LSOAs exhibit disproportionate patient-to- to-popllation ratios. For instance, Forest of Dean and Shropshire, located near the Welsh border, show lower patient numbers, likely due to residents being registered with Welsh GPs. Similarly, Richmondshire, housing a military base in Catterick Garrison, and Forest Heath in Suffolk, hosting RAF Mildenhall, exhibit lower Oxford and Cambridge have higher patient-to-populaat populatiulon ratios, attributed to students registered with local GPs but not counted as residents (the LSOA with highest residual is Oxford 006F, featuring student housing for Oxford Brookes University, as well as International Language Campus).
ratios. Conversely,
Additionally, our analysis highlights broader factors contributing to healthcare inequalities. The residual values, representing deviations from the linear fit, correlate with deprivation levels (r = .16. p R 0 for mid-deprived areas; r .22, p for highly-deprived areas), suggesting greater burden on healthcare access in socioeconomically disadvantaged regions (e.g., one of the areas with the highest coffent-to-populaaionn ratio is near Bolton, a highly deprived region in Greater Manchester). Moreover, the residual values exhibit negative correlation (r = -.40, p 0) with the percentage of White population, indicating disparities associated with ethnic backgrounds. These findings underscore the presence of healthcare access incqualities across the country, as corroborated by prior
16

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Po Porsbaece www.eeeaccoooaooo...o.oo
Figure 7: Visualization of the Drugbank network: showcasing the comprehensive interconnections between various drugs; the inset provides a zoom-in view to the subset specifically associated with anxiety prescriptions.
total population
registered patients
residual
9.0
0
8.5
-2
8.0
-3
7.5
-5
7.0
Figure Health access disparity analysis using MEDSAT. We present quick analysis of health access disparities by examining the relationship between the population and the number of registered patients (log transformed), revealing a strong correlation (r 0.87, p = 0). However, the residual values from a linear fit between the two highlight clear rural-urban pattern, with urban areas showing higher patient-to to-polllttion ratio, which correlates with the percentage of White population (r = -0.40, x 0). Furthermore, the residuals show associations with mid- (r = 0.16, p R 0) and highly-deprived areas (r 0.22, p Rs 0), indicating potentially greater health-access burden in these areas. Details of the analysis are found in Appendix.
research examining various healthcare system aspects, including NHS data on waiting times, staffing, hospital activity, outcomes, and the GP Patient Survey [41], or COVID-19 hospitalisations and deaths [28].
E.2
Predicting Prescriptions
E.2.1
Spatial Lag Model (SLM)
The Spatial Lag Model (SLM) [3, 4], differs from the Ordinary Least Squares (OLS) model by the inclusion the spatial lag term (pWy):
y pW + X3 +
17

---

where is the outcome, X' is the matrix of features, and e is the error term. W is a matrix of the shape N N and it contains spatial weights capturing the spatial interaction among variables, and the coefficient of spatial autocorrelation is captured by p. We fed in input to SLM the ISOA shapefiles calculate the spatial lag term.
Table 6: The spatial Ri² scores resulting from the Spatial Lag Model (SLM). These scores are computed across various prescription types and combinations of dataset features specifically for the year 2020.
metabolic
mental
respiratory asthma
input
diabetes
hypertension
depression
anxicty
opioids
total
Image
0.04
0.23
0.23
0.19
0.25
0.28
0.11
0.13
0.34
0.41
0.37
0.34
0.49
0.25
Soc.
0.33
0.44
0.50
0.48
0.38
0.52
0.31
Env. + Soc. Image
0,38
0.49
0.54
0.55
0.43
0.63
0.38
The SLM results are presented in Table 6.
E.2.2 Machine Learning Models
56ON
24
31315
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3
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11441585
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4242110ER
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53
20013333
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52'N
110151111111111
51ºN
44344
3
5
214
55
50'N
244
6"W
4
2
0"
2
Figure 9: Visualization of example spatial fold created using blockCV spatial blocking: chose hexagonal blocks and randomly assigned those blocks to folds consisting of an equal number of blocks. The numbers specify the fold assignment of the blocks.
Spatial Cross-Valll Validation In spatial data scenarios, traditional random cross-validation methods can inadvertently undervalue prediction errors which can lead suboptimal model choices. Recognizing this challenge, specific validation techniques tailored for spatial modeling, such as spatial blocks
18

---

and buffers. have been introduced [23, 43]. We utilized the R package blockCV, which facilitates the creation of spatially or environmentally separated folds. We used cv_spatial function to create spatial blocks (hexagons) and relied on the interactive tool rangeExplorer withing blockCV to visualise the blocks and assess the impact of block size on the number and arrangement of blocks in the landscape. For our experiments, we chose the block size of 28km x 28km.
Prescription Prediction To understand whether the prevalence scores for the different prescription types can be reliably predicted based on the environmental, sociodemographic indicators and the simple image features, we performed spatial cross- cross-validationn for the LightGBM [22] tree-based model and Feed-Forward Neural Network (FNN). For this experiment, we dropped the instances having missing values. The hyperparameters used for LightGBM model and the FNN are provided in Tables 7 and 8, respectively. For both years in dataset, We randomly created 5 splits consisting of 5 folds where each fold contains an equal number of the above-described spatial blocks. Figure 9 depicts one example of a spatial split consisting of 5 folds. For each split, we performed the standard cross-validation procedure such that 80 % of the spatial blocks are used in model training and the rest 20 % for model testing. This procedure ensures that the test set does not contain LSOAs from the geographical blocks used for model training. Further, we used half of the instances in the test fold and the other half was used as test set to evaluate the model performance. Additionally, in each split, we performed feature standardization such that each feature has zero mean and unit variance. Figure 10 shows the average R² scores over the test sets and the corresponding standard deviation per prescription type and machine learning model. These results show that for both years, the LightGBM model consistently outperforms the FNN. Moreover, we can also see that the R2 scores are similar for the different prescription types for both years except for the total prescriptions which have lower goodness-of-ff in 2019 compared to 2020.
implemented the following evaluation procedure:
validation set for early stopping to prevent overfitting,
Table 7: LightGBM hyperparameters.
parameter
value
objective
regression
metric
rmse
boosting
gdbt
sas__samlee_sstee
bagging
num -terrtttts
100
learning rate
0.1
tree leamne
serial
early Stooy_stopping
.toudd rounds
10
Table 8: Feed-Forward Neural Network hyperparameters.
parameter
value
hidden layers
3
embedding dimension
512
epochs criterion
100
mse
optimizer learning rate weight_decap
Adam
0.0001
0.001
carly_stopping_rouue
10
Detailed SHAP results The 10 most important features as estimated by the SHAP approach [25] and the relationship between feature values and their SHAP importance for the prediction of LightGBM models for 2019, and 2020 are given Figures 11 and 12, respectively. The LightGBM models were trained on the proposed spatial split described in Section F.2. Although these plots indicate that the majority of the relevant factors overlap for 2019 and 2020, we also see that the important factors for a condition can change in the subsequent year. For instance, the acrosols feature
19

---

prescription prediction for 2019
prescription prediction for 2020
0.7
model
0.7
model
0.6
LightGBM
0.6
LightGBM
FNN
fNN
0.5
R² score
R2 score
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
disbetes
hypertenslon
depression
anxiety
oploids
asthma
total
diabetes
hypertenslon
depression
anxiety
uploids
asthma
total
condition
condition
Figure 10: Prescription prediction results on the spatial cross- validaaiio splits of the LightGBM model and the FNN model for the different medical conditions in 2019 (left plot) and 2020 (right plot). The error bars indicate the standard deviation per machine learning model and medical condition and they show that the R scores deviate strongly for the FNN model.
that does not appear among the top-15 15 features for predicting opioids in 2019 is the most important feature for the same condition in 2020. Also, we observe that the contribution of a feature can vary over the years. For example, while low values of thermal radiation are related with an increase in opioids prescriptions for 2019, such relationship is not exhibited for 2020. These insights shed light on the challenges introduced with our dataset for predicting future health outcomes based on historical data.
SHAP Depedence Plots The SHAP dependence plots explain the effect of feature on the model predictions on the entire dataset while also revealing the interaction effects between the features [2]. In Figure 14, we show the SHAP dependence plots for the lightGBM models trained for predicting anxiety and opioid prescriptions. For creating the plots, we used the SHAP python library with the auto option for interaction index that selects the feature which has the highest estimated interaction with the most important feature (according the SHAP values). The left plot shows that LSOAs with high number White ethnicity population result in higher model predictions for anxicty. Further, the interaction of White ethnicity with work from home feature reveals that among the LSOAs with a large White population, lower prescriptions are predicted for those having large percentage of people working from home. Next, the right plot shows that the model tends predict lower prescription rates for LSOAs with high PM2.5 values while at the same time depicting no clear relationship between the PM2.5 and the wind component.
E.3 Temporal Analyses
As Figures 15, and 16 show, both the prescription quantities for our observed outcomes, as well as the environmental point features exhibit different distributions between 2019 and 2020. People in England were prescribed more mental health-related medications, as well as diabetes-associated medications, and less medications associated with asthma and hypertension. Satellite data measured significantly less NO2, ozone. and PM2.5 across England during 2020. The changes in land cover manifested by an increase in built environment, and decrease in trees cover. Finally, temperatures and the amount of solar radiation, as well as the amounts of wind, as expressed through its both components, cast-west and north-south, were higher England in 2020 compared 2019.
References
[1] UK Office for National Statistics. htttps://wet http:///wwwn.oiiwwbbbbbb.. co. uk. wu/ alk/source/////// wttps/eeensss_eeeeee 2021, 2022. [Online; accessed 26-February-2022
20

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diabetes prescriptions
hypertension prescriptions
Hlgh
Hlgh
prof, occupation
White ethnic ity born in the UK Mixed othnicit bt commute by bicycle
commute by birycle
Adian othnicity
work from home
Feature value
Feature value
students moved
themal radiation
prot. occupation
layer temperature
a health
ethnicity
vegatation
very good health
commute
Low
SHAP
SHAP
anxiety prescriptions
depression prescriptions
High
High
NO2
White ethnicity
UK resident 10+ years
NO2
thermal radiation
Mixed ethnicity
work from home
Feature value
UK resident 10+ years
Feature value
very good health
wast-west wind
Mixed ethnicity
prof. occupation
azone
born
prot. accupation
from home
сenpy evaporation
very health
SHAP
SHAP
opioids prescriptions
asthma prescriptions
High
NO2
White othnic
themal radiation
NO2
UK resident 10+ years
born In the UK thermal tnertioradiation
ozone
Feature value
Feature value
Mixed ethnicity
Mixed ethnic ily prot. occupation commute by bicycle
prot. accupation
work from home
PM2.5
high vegetation LA)
very good health
work travel Zkm and Skm
ethnicity
temperature
10
SHAP
SHAP
Figure SHAP values for the LightGBM model used for predicting prescriptions for different conditions in MEDSAT for 2019. The SHAP value for a feature indicates its contribution towards the difference between the prediction for an instance and the average model prediction.
[2] Census income classification with LightGBM https://shap:: rew.aapprraahhhooc.siooo httpp://ssappreddhhecc..bbbb http://ww...poooooo//aaaaaaaaaee
io/en// wwwpaaappleentteboooosttaaaaaaaa eeaaaaaeeeeeaaeeeeeeeeeeeee httpaeeeeeeeeeebbbdbbeeeeee b_w.abass______ "tl.mmddls/eeessee
ou 02-August-2023).
2023. [Online; accessed
21

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diabetes prescriptions
hypertension prescriptions
High
High
prof. occupation
White ethnicity born in the UK
work from home
students moved
NO2
commute by bicycle
reature value
prof. occupation othnic fty cast-west wind
Feature value
east-west
PW2.5
ethnicity
very good
vegetation
evaporation
work travel and
PM2.5
Low
SHAP
SHAP
anxiety prescriptions
depression prescriptions
High
High
NO2
White ethnicity
White ethnicity
NO2
work from home
work from home
canapy evaporation
Feature value
UK resident 10+ years
Feature value
thermal rediation
prof. eccupation
east-west wind
east-west wind
very good health
very good health
Mixed othnicity
Mixed athnicity
health
evaporation
bedo
SHAP
SHAP
opioids prescriptions
asthma prescriptions
High
UK resident 10+ years
White othnicity
aerosols
NO2
NO2
commute by bus east-west wind prof. occupation bare soil eveporation
work from home
Feature value
Feature value
White ethnicity
cast west wind
Mixed ethnicity
born the
bad health
lake bottom temperature
prof. accupation
radiation
skin reservoir,
2.5
SHAP
SHAP
Figure SHAP feature importance for the LightGBM model used for predicting prescriptions for different conditions in MEDSAT for 2020.
[3] Luc Anselin. Spatial econometrics: Methods and Models, volume 4. Springer Science & Business Media, 1988.
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22

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opioids prescriptions
High
Image_ Lange_Engladd..om JJA2200 806
"mage_England DJF20192220
image_ England DJF 20192020 std
mmage_Englard__
Feature value
maage_nngland.cmm _arp____222_____e_ea (ee00__ea_____ B11
Baage__Engnnd_jAaa0 _AA0_0/_22s__s_ std__12
"mage_England
Ewglandd__A222222_moo std_611
image_ England __l0_o_22______
image_ Lunge_England__ 102020_mean___
imago_England Englandg DJF20192020_meaa maao:/000mma_BB0000
Image
Low
SHAP
Figure 13: SHAP values for the LightGBM model trained on prescription prediction based on the mean and standard deviation simple image features.
Dependence Poot for anciety prescriptions
Dependence Plat for opiaids prescriptions
SHAP value for White attricity
SHAP value for PM2.5
0.55
north-soo h-uuh wina
0.35
0.50
0.20
0.80
0.15
0.75
0.0
02
0.6
0.6
White othnicity
PM225
Figure 14: SHAP dependence plots for anxiety prescriptions (left) and opioids prescriptions (right).
space and mortality in european cities: a health impact assessment study. The Lancet Planetary Health, 5(10)=e718-e730 2021.
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diabetes
opioids
0.03
0.02
2019
Density
2020
0.02
0.01
0.01
0.00
0.00
50
100
50
100
150
Quantity Per Capita
Quantity Per Capita
asthma
hypertension
0.075
2019
0.010
Density
2020
0.050
0.005
0.025
0.000
0.000
Quantity Per Capita
Quantity Per Capita
depression
anxiety
0.03
2019
0.03
Density
0.02
2020
0.02
0.01
0.00
0
20
40
60
80
20
40
60
80
Quantity Per Capita
Quantity Per Capita
Figure 15: Differences in distributions of MEDSAT outcomes between 2019 and 2020. The Kolmogorov- Somiino (KS) test [27] showed that, except for opioids, all the other prescription types have significantly different distributions between the two years. Notably, prescriptions associated with hypertension and asthma have decreased in 2020; while the prescriptions associated with mental conditions (i.e., depression and anxiety), as well diabetes have increased.
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NO2
0z0ne
aerosols
1.5 169
PM2.5
40000
(2019)
100
(2020)
0.4
20000
1000
25
0.00005
0.00010 0.00016
0.0
325
330
0.10
0.14
0.0
5.0
7.5
greenery
vegetation
(2018)
(2020)
Density
5
0.0
0.5
1.0
0.00
0.25
0.50
0.0
0.5
temperature
1e-8solar radiation
east west wind
north south wind
3
0.8
(2019)
2.0
(2020)
Density
0.4
1.0
0.2
0.0
280.0
0.0
Figure 16: Differences in distributions of MEDSAT environmental features between 2019 and 2020. The Kolmogorov-Smimat (KS) test [27] showed that, except for low and high vegetation LAI, all the other environmental features have significantly different distributions between the two years. Specifically, variables related to air pollution (NO2, ozone, and PM2.5) have significantly decreased in 2020. Interestingly, the amount of total aerosols has on the other hand increased. The amount of trees as observed from satellite imagery has decreased, while the amount of built environment has increased. Finally, all the climate- relatte variables, such as temperature, solar radiation, the two wind components have also exhibited increased values 2020.
[18]
Lisa EM Hopcroft, Jon Massey, Helen J Curtis, Brian Mackenna, Richard Croker, Andrew D Brown, Thomas O'Dwyer, Orla Macdonald, David Evans, Peter Inglesby, et al. Data-driven identification of unusual prescribing behavior: Analysis and use of interactive data tool using months of primary care data from 6500 practices in england. JMIR Medical Informatics, 11:e44237, 2023.
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Reviews:
[{"review_id": "AEcF0QaPjG", "reviewer": "Reviewer_AAen", "summary": "", "questions": "", "limitations": "- The linkage of drugs to condition is coarse and imperfect, e.g., why are opioids binned under the \"mental\" class?  The appendix only seems to include drugs associated with diabetes so it's not possible to sanity check some of the assignments. The appendix acknowledges \"a single API or medical drug can be used to treat various conditions\" That seems a serious limitation of the paper's analyses for any conclusion involving a specific disorder.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Carefully curated datasets for social determinants of health are critical valuable for health research.\n- Satellite imaging is an exciting modality to link in a geospatial/temporal setting", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "7RvPGbjpdx", "reviewer": "Reviewer_nQhp", "summary": "", "questions": "", "limitations": "The discussion is reasonable. See also questions in the additional feedback section. The literature on environmental exposures (including air quality) in hospitalization is massive, and it makes sense to focus on those related to satellite imagery.", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "- The dataset facilitates accessing prescription data from the NHS in the UK, which needs substantial processing, making it more useful for future research.\n- Imagery data is hard to process and, therefore, seldom used. Making it more accessible can positively impact public health studies.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "pomCzW9aM0", "reviewer": "Reviewer_xh3o", "summary": "", "questions": "", "limitations": "Yes, the authors adequately address limitations and potential negative societal impact.", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "Please find a list of strengths below:\n\n1. The motivations are strong for the creation of MedSat.\n2. Incorporation of data prior to and during COVID allows for disentanglement of COVID effects.\n3. Comprehensive discussion of related work and how MedSat complements existing resources.\n4. The authors clearly describe how they curated the MedSat dataset.\n5. The dataset is anonymized, alleviating potential ethical concerns.\n6. Dataset use cases are clearly described.\n7. The authors conduct an interesting analysis of predicting prescription rates using MedSat. In addition, the authors use SHAP to analyze the contribution of different input features. This analysis revealed interesting patterns in the dataset and serves as a fantastic example for how other researchers can use MedSat to study the interaction of health, the environment, and sociodemographic factors.\n8. The authors include a compelling discussion of limitations and future work.\n9. The figures are useful and visually appealing. FIgures 2, 3, and 4, for instance, do a fantastic job of demonstrating the breadth, depth, and variability of the dataset.\n10. MedSat has a permissive license.\n11. Code to recreate the dataset is provided.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "sSxPFEzVpa", "reviewer": "Reviewer_Zvuw", "summary": "", "questions": "", "limitations": "Please refer to the above paragraph.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The paper presents a novel and comprehensive dataset that integrates medical prescriptions, environmental indicators, and sociodemographic factors for studying public and population health in England. The dataset is a valuable contribution to the research community and has many implications for machine learning and public health.\n2. The paper shows the potential applications of the proposed dataset, such as predicting prescriptions, revealing health disparities, and uncovering health factors using machine learning models.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The paper provides a well-written blueprint to repurpose satellite images for public health use by linking them with other data sources.

The reviewers described a number of strengths of this work. For example, the dataset is well-motivated and valuable for health research, the combination of satellite imaging and additional data sources is well done, and the substantial processing of the data will be beneficial for future research.

The authors made significant effort to address all reviewer concerns during the discussion period. Zvuw did not give a positive rating, but this does not take into account the author discussion and is thus weighted less.

Overall, I recommend acceptance.
Decision: Accept (Poster)