postcode-prognosis/PostcodePrognosis_Explorer.html at main · Mulualem03/postcode-prognosis · GitHub

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<div class="container">

<header>
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    <div>
      <div class="brand-name">Postcode Prognosis</div>
      <div class="brand-tag">Health Inequalities · England LAs</div>
    </div>
  </div>
  <div class="meta">
    Mulualem Kahssay<br>
    May 2026<br>
    <strong>Personal Project</strong>
  </div>
</header>

<nav class="tabs" id="tabs">
  <button class="tab active" data-tab="overview">Overview</button>
  <button class="tab" data-tab="lookup">LA Lookup</button>
  <button class="tab" data-tab="gradient">The Deprivation Gradient</button>
  <button class="tab" data-tab="model">The Model</button>
  <button class="tab" data-tab="residuals">Residuals</button>
  <button class="tab" data-tab="public-good">Public Good</button>
</nav>

<!-- ============= OVERVIEW ============= -->
<div class="panel active" data-panel="overview">
  <div class="eyebrow">The question</div>
  <h1>How much of England's variation in <em>avoidable</em> hospital admissions is just <em>postcode</em>?</h1>
  <p class="lede">
    Across 309 Local Authorities in England, emergency admissions for ambulatory care sensitive conditions, things like asthma, COPD, diabetes and heart failure, vary by more than 3-fold. This is a model that quantifies how much of that variation is explained by deprivation, age, and primary care quality, and what the residual variation tells us about who is getting better or worse care than their demographics would predict.
  </p>

  <div class="stat-grid">
    <div class="stat">
      <div class="stat-label">ACS admissions modelled</div>
      <div class="stat-value blue" id="stat-total">1.02M</div>
      <div class="stat-foot">Across 309 Local Authorities, 2023</div>
    </div>
    <div class="stat">
      <div class="stat-label">England-wide rate</div>
      <div class="stat-value" id="stat-rate">1,742</div>
      <div class="stat-foot">per 100,000 population</div>
    </div>
    <div class="stat">
      <div class="stat-label">Deprivation R²</div>
      <div class="stat-value accent" id="stat-r2">51%</div>
      <div class="stat-foot">Variation explained by IMD alone</div>
    </div>
    <div class="stat">
      <div class="stat-label">Avoidable cost</div>
      <div class="stat-value rust" id="stat-cost">£259m</div>
      <div class="stat-foot">If above-median LAs hit median rate</div>
    </div>
  </div>

  <div class="card">
    <div class="card-title"><span class="dot accent"></span><h2 style="margin:0">What this analysis does</h2></div>
    <p style="color:var(--slate); margin:8px 0 12px">
      A Poisson generalised linear model is fit to admission counts per Local Authority, with population as the offset. Predictors are deprivation (IMD 2019), age structure, GP practice quality (QOF), urban/rural classification, and region. A Random Forest serves as a non-linear comparator. Spatial cross-validation (leave-one-region-out) tests whether predictions hold up away from the training data.
    </p>
    <h3>Five questions the analysis answers</h3>
    <ol style="color:var(--slate); padding-left:22px">
      <li>How much of the variation in ACS admission rates between Local Authorities is explained by deprivation alone?</li>
      <li>What does the gradient look like across the IMD deciles, and how big is the gap between the most and least deprived?</li>
      <li>Once we adjust for deprivation, age, and primary care quality, which LAs still show much higher (or lower) rates than predicted?</li>
      <li>Does the model generalise across regions, or does it fail when held out on unseen areas? (TRIPOD-style honesty.)</li>
      <li>If above-median LAs were brought to the national median, how many admissions and how much money would that save?</li>
    </ol>
  </div>

  <div class="note">
    <div class="note-title">Disclosure</div>
    This analysis uses a synthetic dataset of 309 English Local Authorities, calibrated against the marginal distributions and bivariate associations published in:
    King's Fund (2012) ACS data briefing, Nuffield Trust QualityWatch (2014), Franklin et al. BMJ Paediatrics Open (2025), and Tian et al. (2014). Coefficient magnitudes are illustrative; methodology and code transfer directly to the live OHID Fingertips API.
  </div>
</div>

<!-- ============= LA LOOKUP ============= -->
<div class="panel" data-panel="lookup">
  <div class="eyebrow">Look up your area</div>
  <h2>The model's view of any English Local Authority</h2>
  <p style="color:var(--slate); margin-bottom:24px">
    Type or pick a Local Authority. The tool shows its deprivation, demographics, observed ACS admission rate, the model's predicted rate, and the residual: how much above or below the prediction it sits.
  </p>

  <div class="lookup">
    <div>
      <label>Local Authority</label>
      <input type="text" id="la-search" placeholder="Start typing, e.g. Newcastle, Leeds, Wandsworth..." autocomplete="off" />
    </div>
    <div>
      <label>Or pick from list</label>
      <select id="la-select"></select>
    </div>
  </div>

  <div class="la-result" id="la-result"></div>
</div>

<!-- ============= GRADIENT ============= -->
<div class="panel" data-panel="gradient">
  <div class="eyebrow">Insight 1 of 4 · The deprivation gradient</div>
  <h2>Half the story is just deprivation.</h2>
  <p style="color:var(--slate); max-width:720px">
    The Pearson correlation between IMD score and ACS admission rate is <strong>0.71</strong>. Deprivation alone explains roughly <strong>51%</strong> of the variation between Local Authorities. The most deprived decile sees rates <strong>1.86×</strong> higher than the least deprived.
  </p>

  <div class="card">
    <h3>Mean admission rate by IMD decile</h3>
    <div class="barchart" id="decile-bars"></div>
  </div>

  <div class="card">
    <h3>Mean admission rate by region</h3>
    <div class="barchart" id="region-bars"></div>
  </div>

  <div class="note">
    <div class="note-title">What this tells us</div>
    The gradient is steep but not deterministic. Two LAs with the same IMD score can have admission rates 1,000 per 100k apart. Deprivation is the single biggest lever but it is not the only one, and residual variation is where service-level intervention can target.
  </div>
</div>

<!-- ============= MODEL ============= -->
<div class="panel" data-panel="model">
  <div class="eyebrow">Insight 2 of 4 · The predictive model</div>
  <h2>Five predictors, one Poisson GLM, R² of 0.67.</h2>
  <p style="color:var(--slate); max-width:720px">
    Predictors: deprivation, age structure, primary care quality, urbanity, and region. The model achieves R² = 0.67 on the full data, and a Random Forest comparator achieves R² = 0.60 under 5-fold cross-validation. The simpler linear model is competitive, suggesting the underlying relationship is largely linear on the log-rate scale.
  </p>

  <div class="card">
    <h3>Rate ratios for the key predictors</h3>
    <p style="color:var(--muted); font-size:13px; margin:6px 0 14px">
      Each rate ratio is the multiplicative effect on the admission rate for a 1-unit change in the predictor, holding others constant. Bands show 95% confidence interval.
    </p>
    <div class="tbl-wrap">
      <table id="coefs-table">
        <thead>
          <tr>
            <th>Predictor</th>
            <th>Plain English</th>
            <th class="num">Rate ratio</th>
            <th class="num">95% CI</th>
            <th class="num">p</th>
          </tr>
        </thead>
        <tbody></tbody>
      </table>
    </div>
  </div>

  <div class="card">
    <h3>Spatial cross-validation: leave-one-region-out</h3>
    <p style="color:var(--muted); font-size:13px; margin:6px 0 14px">
      A fair test of generalisability: fit on 8 regions, predict the 9th. Performance varies dramatically by which region is held out, an honest limitation worth flagging in any report.
    </p>
    <div class="tbl-wrap">
      <table id="cv-table">
        <thead>
          <tr>
            <th>Held-out region</th>
            <th class="num">LAs</th>
            <th class="num">R²</th>
            <th class="num">MAE / 100k</th>
          </tr>
        </thead>
        <tbody></tbody>
      </table>
    </div>
  </div>
</div>

<!-- ============= RESIDUALS ============= -->
<div class="panel" data-panel="residuals">
  <div class="eyebrow">Insight 3 of 4 · The unexplained 33%</div>
  <h2>Who beats their demographics?</h2>
  <p style="color:var(--slate); max-width:720px">
    After adjusting for IMD, age, QOF, urban/rural, and region, residuals span +/- 30%. Areas with rates much higher than predicted may indicate weaknesses in primary or community care; areas with rates much lower may hold lessons for replication.
  </p>

  <div style="display:grid; grid-template-columns:1fr 1fr; gap:18px; margin-top:18px">
    <div class="card">
      <div class="card-title"><span class="dot red"></span><h3 style="margin:0">Worse than predicted</h3></div>
      <p style="color:var(--muted); font-size:13px">10 LAs whose actual ACS admission rate exceeds the model's prediction by the largest margin.</p>
      <div class="tbl-wrap">
        <table id="over-table">
          <thead><tr><th>Local Authority</th><th>Region</th><th class="num">Excess</th></tr></thead>
          <tbody></tbody>
        </table>
      </div>
    </div>
    <div class="card">
      <div class="card-title"><span class="dot green"></span><h3 style="margin:0">Better than predicted</h3></div>
      <p style="color:var(--muted); font-size:13px">10 LAs whose actual rate is lower than the model predicts. Potential replication targets.</p>
      <div class="tbl-wrap">
        <table id="under-table">
          <thead><tr><th>Local Authority</th><th>Region</th><th class="num">Shortfall</th></tr></thead>
          <tbody></tbody>
        </table>
      </div>
    </div>
  </div>
  <style>
    @media (max-width:900px){.panel[data-panel=residuals] > div{grid-template-columns:1fr !important}}
  </style>
</div>

<!-- ============= PUBLIC GOOD ============= -->
<div class="panel" data-panel="public-good">
  <div class="eyebrow">Insight 4 of 4 · The case for action</div>
  <h2>Equalising to the median saves £259 million per year.</h2>
  <p style="color:var(--slate); max-width:720px">
    A simple counterfactual. If every Local Authority above the national median ACS admission rate could be brought down to the median, how many admissions, and how much spend, would that avoid?
  </p>

  <div class="stat-grid">
    <div class="stat">
      <div class="stat-label">Median LA rate</div>
      <div class="stat-value blue" id="pg-median">1,627</div>
      <div class="stat-foot">per 100,000</div>
    </div>
    <div class="stat">
      <div class="stat-label">LAs above median</div>
      <div class="stat-value" id="pg-above">154 / 309</div>
      <div class="stat-foot">half the country</div>
    </div>
    <div class="stat">
      <div class="stat-label">Excess admissions</div>
      <div class="stat-value rust" id="pg-excess">144k</div>
      <div class="stat-foot">per year vs median benchmark</div>
    </div>
    <div class="stat">
      <div class="stat-label">Annual saving</div>
      <div class="stat-value green" id="pg-cost">£259m</div>
      <div class="stat-foot">at £1,800 per admission</div>
    </div>
  </div>

  <div class="card">
    <h3>Where the savings would land</h3>
    <p style="color:var(--muted); font-size:13px; margin-bottom:12px">
      The largest avoidable admission counts sit in the highest-rate LAs. Equalising the top decile alone would deliver about half the national saving.
    </p>
    <div class="barchart" id="savings-bars"></div>
  </div>

  <div class="note">
    <div class="note-title">A note on causation</div>
    "Equalising to the median" is a counterfactual exercise, not a forecast. It quantifies the size of the prize, not the policy lever to capture it. The 33% of variation that is residual after demographic adjustment is the practically actionable signal: that's where the model points us toward case studies of high and low performers.
  </div>

  <div class="card" style="margin-top:24px">
    <h3>What I would do next, given more time</h3>
    <ul style="color:var(--slate); padding-left:22px">
      <li><strong>Pull live OHID Fingertips data</strong> via API and re-run the pipeline against real numbers, replacing the synthetic dataset.</li>
      <li><strong>Drop to MSOA-level granularity</strong>, ~7,000 areas instead of 309 LAs, to pick up within-LA variation.</li>
      <li><strong>Decompose by condition</strong> (asthma, COPD, diabetes, heart failure) to see whether the deprivation gradient differs by clinical category.</li>
      <li><strong>Add a temporal axis</strong>, tracking rate-of-change in admissions against changes in QOF performance over 2018-2023.</li>
      <li><strong>Geographically Weighted Regression</strong> to allow coefficients to vary across space, which would address the spatial CV weakness directly.</li>
      <li><strong>Causal layer</strong>: an instrumental variable design using changes in the GP funding formula to identify the marginal effect of primary care investment on ACS admissions.</li>
    </ul>
  </div>
</div>

<footer>
  Postcode Prognosis · methodology aligned with TRIPOD reporting for prediction models · May 2026
</footer>

</div>

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"la_count": 30, "pop_total": 6614693, "admissions_total": 127765, "rate_mean": 1912.2, "imd_mean": 21.0, "rate_weighted": 1932.0}, {"region": "East Midlands", "la_count": 35, "pop_total": 6445815, "admissions_total": 107048, "rate_mean": 1715.2, "imd_mean": 20.4, "rate_weighted": 1661.0}, {"region": "East of England", "la_count": 45, "pop_total": 7827921, "admissions_total": 132789, "rate_mean": 1636.5, "imd_mean": 14.8, "rate_weighted": 1696.0}, {"region": "London", "la_count": 33, "pop_total": 4861600, "admissions_total": 77270, "rate_mean": 1573.9, "imd_mean": 18.4, "rate_weighted": 1589.0}, {"region": "South East", "la_count": 64, "pop_total": 13268642, "admissions_total": 205734, "rate_mean": 1525.9, "imd_mean": 13.1, "rate_weighted": 1551.0}, {"region": "South West", "la_count": 30, "pop_total": 5268292, "admissions_total": 78784, "rate_mean": 1462.2, "imd_mean": 12.9, "rate_weighted": 1495.0}], "by_decile": [{"imd_decile": 1, "la_count": 31, "rate_mean": 1291.0, "rate_p25": 1085.0, "rate_p75": 1481.0}, {"imd_decile": 2, "la_count": 31, "rate_mean": 1379.0, "rate_p25": 1149.0, "rate_p75": 1530.0}, {"imd_decile": 3, "la_count": 31, "rate_mean": 1469.0, "rate_p25": 1304.0, "rate_p75": 1604.0}, {"imd_decile": 4, "la_count": 31, "rate_mean": 1575.0, "rate_p25": 1412.0, "rate_p75": 1698.0}, {"imd_decile": 5, "la_count": 31, "rate_mean": 1703.0, "rate_p25": 1483.0, "rate_p75": 1867.0}, {"imd_decile": 6, "la_count": 30, "rate_mean": 1589.0, "rate_p25": 1460.0, "rate_p75": 1785.0}, {"imd_decile": 7, "la_count": 31, "rate_mean": 1758.0, "rate_p25": 1529.0, "rate_p75": 2013.0}, {"imd_decile": 8, "la_count": 31, "rate_mean": 1841.0, "rate_p25": 1562.0, "rate_p75": 2114.0}, {"imd_decile": 9, "la_count": 31, "rate_mean": 2040.0, "rate_p25": 1826.0, "rate_p75": 2301.0}, {"imd_decile": 10, "la_count": 31, "rate_mean": 2396.0, "rate_p25": 2046.0, "rate_p75": 2761.0}], "top10_highest": [{"la_name": "Walsall", "region": "West Midlands", "imd_score": 41.7019875614867, "pct_65_plus": 26.0702587078142, "acs_admissions_per_100k": 3491.1}, {"la_name": "Chorley", "region": "North West", "imd_score": 47.833662203871725, "pct_65_plus": 20.84457698207157, "acs_admissions_per_100k": 3318.2}, {"la_name": "Carlisle", "region": "North West", "imd_score": 36.55761148084581, "pct_65_plus": 22.0485035898388, "acs_admissions_per_100k": 3296.2}, {"la_name": "Redditch", "region": "West Midlands", "imd_score": 35.570772344580945, "pct_65_plus": 18.482029936705924, "acs_admissions_per_100k": 2979.8}, {"la_name": "North Tyneside", "region": "North East", "imd_score": 41.4514319282527, "pct_65_plus": 13.02593744286228, "acs_admissions_per_100k": 2893.2}, {"la_name": "Gedling", "region": "East Midlands", "imd_score": 33.534691962606644, "pct_65_plus": 25.550995083808058, "acs_admissions_per_100k": 2783.7}, {"la_name": "Barnsley", "region": "Yorkshire and The Humber", "imd_score": 32.39087240579574, "pct_65_plus": 22.48516379626235, "acs_admissions_per_100k": 2780.2}, {"la_name": "Harrogate", "region": "Yorkshire and The Humber", "imd_score": 48.39080218653682, "pct_65_plus": 21.468395353086382, "acs_admissions_per_100k": 2780.1}, {"la_name": "Gateshead", "region": "North East", "imd_score": 34.85295008067881, "pct_65_plus": 21.778190334523657, "acs_admissions_per_100k": 2741.3}, {"la_name": "East Northamptonshire", "region": "East Midlands", "imd_score": 34.72775062650568, "pct_65_plus": 21.283864305277927, "acs_admissions_per_100k": 2701.3}], "top10_lowest": [{"la_name": "Chiltern", "region": "South East", "imd_score": 4.0, "pct_65_plus": 16.606185807869977, "acs_admissions_per_100k": 902.8}, {"la_name": "Worthing", "region": "South East", "imd_score": 7.349623938147908, "pct_65_plus": 20.44359003869559, "acs_admissions_per_100k": 976.9}, {"la_name": "New Forest", "region": "South East", "imd_score": 4.294518759243771, "pct_65_plus": 25.258068853870235, "acs_admissions_per_100k": 988.4}, {"la_name": "Dacorum", "region": "East of England", "imd_score": 7.952656850854763, "pct_65_plus": 10.734046452717886, "acs_admissions_per_100k": 999.1}, {"la_name": "Cotswold", "region": "South West", "imd_score": 4.386387911057533, "pct_65_plus": 16.49401342594384, "acs_admissions_per_100k": 1003.4}, {"la_name": "Trafford", "region": "North West", "imd_score": 18.401982364369925, "pct_65_plus": 8.902887924718343, "acs_admissions_per_100k": 1020.5}, {"la_name": "Mendip", "region": "South West", "imd_score": 5.785450410483927, "pct_65_plus": 24.6608376511117, "acs_admissions_per_100k": 1031.4}, {"la_name": "Bournemouth, Christchurch and Poole", "region": "South West", "imd_score": 4.0, "pct_65_plus": 7.0, "acs_admissions_per_100k": 1041.3}, {"la_name": "Blackburn with Darwen", "region": "North West", "imd_score": 15.206699651460346, "pct_65_plus": 16.87997634986269, "acs_admissions_per_100k": 1050.0}, {"la_name": "Southampton", "region": "South East", "imd_score": 8.30226648196175, "pct_65_plus": 16.94273630265121, "acs_admissions_per_100k": 1066.8}], "top10_over": [{"LA": "Bracknell Forest", "Region": "South East", "IMD": 8.2, "Actual rate": 2225.6, "Predicted rate": 1463.0, "Excess %": 52.1}, {"LA": "Dartford", "Region": "South East", "IMD": 12.7, "Actual rate": 1796.2, "Predicted rate": 1284.1, "Excess %": 39.9}, {"LA": "Ashford", "Region": "South East", "IMD": 14.7, "Actual rate": 2208.0, "Predicted rate": 1627.2, "Excess %": 35.7}, {"LA": "East Riding of Yorkshire", "Region": "Yorkshire and The Humber", "IMD": 22.5, "Actual rate": 2217.1, "Predicted rate": 1685.8, "Excess %": 31.5}, {"LA": "Basildon", "Region": "East of England", "IMD": 11.9, "Actual rate": 2185.5, "Predicted rate": 1662.3, "Excess %": 31.5}, {"LA": "Plymouth", "Region": "South West", "IMD": 5.1, "Actual rate": 1641.6, "Predicted rate": 1251.6, "Excess %": 31.2}, {"LA": "Bedford", "Region": "East of England", "IMD": 5.3, "Actual rate": 1930.3, "Predicted rate": 1473.6, "Excess %": 31.0}, {"LA": "Stratford-on-Avon", "Region": "West Midlands", "IMD": 28.6, "Actual rate": 2667.1, "Predicted rate": 2043.1, "Excess %": 30.5}, {"LA": "Arun", "Region": "South East", "IMD": 13.0, "Actual rate": 2042.4, "Predicted rate": 1569.5, "Excess %": 30.1}, {"LA": "Canterbury", "Region": "South East", "IMD": 21.0, "Actual rate": 2138.5, "Predicted rate": 1648.9, "Excess %": 29.7}], "top10_under": [{"LA": "Braintree", "Region": "East of England", "IMD": 8.9, "Actual rate": 1075.0, "Predicted rate": 1702.4, "Shortfall %": -36.9}, {"LA": "Cambridge", "Region": "East of England", "IMD": 20.7, "Actual rate": 1436.4, "Predicted rate": 2143.3, "Shortfall %": -33.0}, {"LA": "Swindon", "Region": "South West", "IMD": 17.4, "Actual rate": 1068.3, "Predicted rate": 1564.1, "Shortfall %": -31.7}, {"LA": "Stockport", "Region": "North West", "IMD": 24.9, "Actual rate": 1615.7, "Predicted rate": 2346.9, "Shortfall %": -31.2}, {"LA": "New Forest", "Region": "South East", "IMD": 4.3, "Actual rate": 988.4, "Predicted rate": 1431.8, "Shortfall %": -31.0}, {"LA": "Wolverhampton", "Region": "West Midlands", "IMD": 21.9, "Actual rate": 1445.2, "Predicted rate": 2074.9, "Shortfall %": -30.3}, {"LA": "Blackburn with Darwen", "Region": "North West", "IMD": 15.2, "Actual rate": 1050.0, "Predicted rate": 1506.8, "Shortfall %": -30.3}, {"LA": "West Devon", "Region": "South West", "IMD": 9.1, "Actual rate": 1108.7, "Predicted rate": 1529.2, "Shortfall %": -27.5}, {"LA": "Trafford", "Region": "North West", "IMD": 18.4, "Actual rate": 1020.5, "Predicted rate": 1399.5, "Shortfall %": -27.1}, {"LA": "Slough", "Region": "South East", "IMD": 32.5, "Actual rate": 1588.1, "Predicted rate": 2147.1, "Shortfall %": -26.0}], "coefs": [{"variable": "const", "coef": -3.2553, "rate_ratio": 0.0386, "p_value": 0.0, "ci_low": 0.0326, "ci_high": 0.0457}, {"variable": "imd_score", "coef": 0.0181, "rate_ratio": 1.0182, "p_value": 0.0, "ci_low": 1.018, "ci_high": 1.0185}, {"variable": "pct_65_plus", "coef": 0.0217, "rate_ratio": 1.022, "p_value": 0.0, "ci_low": 1.0215, "ci_high": 1.0224}, {"variable": "qof_score", "coef": -0.0171, "rate_ratio": 0.983, "p_value": 0.0, "ci_low": 0.9813, "ci_high": 0.9847}, {"variable": "urban", "coef": 0.0463, "rate_ratio": 1.0474, "p_value": 0.0, "ci_low": 1.0423, "ci_high": 1.0525}, {"variable": "rural", "coef": 0.0352, "rate_ratio": 1.0359, "p_value": 0.0, "ci_low": 1.0288, "ci_high": 1.043}, {"variable": "region_East of England", "coef": 0.0697, "rate_ratio": 1.0722, "p_value": 0.0, "ci_low": 1.0635, "ci_high": 1.0811}, {"variable": "region_London", "coef": -0.0403, "rate_ratio": 0.9605, "p_value": 0.0, "ci_low": 0.9516, "ci_high": 0.9694}, {"variable": "region_North East", "coef": 0.1576, "rate_ratio": 1.1707, "p_value": 0.0, "ci_low": 1.1591, "ci_high": 1.1824}, {"variable": "region_North West", "coef": 0.0815, "rate_ratio": 1.0849, "p_value": 0.0, "ci_low": 1.0763, "ci_high": 1.0936}, {"variable": "region_South East", "coef": 0.0144, "rate_ratio": 1.0146, "p_value": 0.0003, "ci_low": 1.0067, "ci_high": 1.0224}, {"variable": "region_South West", "coef": -0.0424, "rate_ratio": 0.9585, "p_value": 0.0, "ci_low": 0.9494, "ci_high": 0.9675}, {"variable": "region_West Midlands", "coef": 0.1068, "rate_ratio": 1.1128, "p_value": 0.0, "ci_low": 1.1037, "ci_high": 1.1219}, {"variable": "region_Yorkshire and The Humber", "coef": 0.0719, "rate_ratio": 1.0745, "p_value": 0.0, "ci_low": 1.0646, "ci_high": 1.0845}], "spatial_cv": [{"held_out_region": "London", "n_las": 33, "r2": 0.6, "mae": 160.31, "mape": 10.65}, {"held_out_region": "Yorkshire and The Humber", "n_las": 21, "r2": 0.54, "mae": 238.63, "mape": 11.77}, {"held_out_region": "West Midlands", "n_las": 30, "r2": 0.53, "mae": 305.22, "mape": 14.67}, {"held_out_region": "North West", "n_las": 39, "r2": 0.52, "mae": 274.43, "mape": 13.89}, {"held_out_region": "South East", "n_las": 64, "r2": 0.4, "mae": 205.38, "mape": 13.21}, {"held_out_region": "South West", "n_las": 30, "r2": 0.33, "mae": 194.52, "mape": 14.39}, {"held_out_region": "East of England", "n_las": 45, "r2": 0.33, "mae": 232.35, "mape": 13.97}, {"held_out_region": "North East", "n_las": 12, "r2": 0.27, "mae": 290.45, "mape": 12.78}, {"held_out_region": "East Midlands", "n_las": 35, "r2": -3.05, "mae": 817.99, "mape": 50.36}]}</script>
<script>
const DATA = JSON.parse(document.getElementById("appdata").textContent);
const fmt = n => Math.round(n).toLocaleString();
const fmtPct = n => (n>=0? "+":"") + n.toFixed(1) + "%";

// ----- Tabs -----
document.querySelectorAll(".tab").forEach(t => {
  t.addEventListener("click", () => {
    document.querySelectorAll(".tab").forEach(x => x.classList.remove("active"));
    document.querySelectorAll(".panel").forEach(x => x.classList.remove("active"));
    t.classList.add("active");
    document.querySelector(`[data-panel="${t.dataset.tab}"]`).classList.add("active");
    window.scrollTo({top:0, behavior:"smooth"});
  });
});

// ----- LA Lookup -----
const select = document.getElementById("la-select");
const sortedLas = [...DATA.las].sort((a,b)=> a.la_name.localeCompare(b.la_name));
const optHTML = ['<option value="">Select a Local Authority...</option>']
  .concat(sortedLas.map(la => `<option value="${la.la_code}">${la.la_name}, ${la.region}</option>`)).join("");
select.innerHTML = optHTML;
select.addEventListener("change", e => { if(e.target.value) renderLa(e.target.value); });

const search = document.getElementById("la-search");
search.addEventListener("input", e => {
  const q = e.target.value.toLowerCase().trim();
  if (!q) return;
  const match = sortedLas.find(la => la.la_name.toLowerCase().startsWith(q));
  if (match) renderLa(match.la_code);
});

function renderLa(code){
  const la = DATA.las.find(x => x.la_code === code);
  if (!la) return;
  const div = document.getElementById("la-result");

  // Position on the gauge: 0 = lowest rate, 100 = highest
  const minR = DATA.summary.rate_min;
  const maxR = DATA.summary.rate_max;
  const gaugePos = ((la.acs_admissions_per_100k - minR) / (maxR - minR)) * 100;

  const residualPill = la.residual_pct > 5 ? "high" : la.residual_pct < -5 ? "low" : "mid";
  const residualLabel = la.residual_pct > 0 ? "above predicted" : "below predicted";

  div.innerHTML = `
    <div class="la-name">${la.la_name}</div>
    <div class="la-region">${la.region} · ${la.rural_urban} · IMD decile ${la.imd_decile}</div>

    <div style="margin:24px 0 8px"><strong>Position vs all 309 LAs</strong></div>
    <div class="gauge">
      <div class="gauge-bar"></div>
      <div class="gauge-marker" style="left:${gaugePos.toFixed(1)}%"></div>
    </div>
    <div style="display:flex; justify-content:space-between; font-size:11px; color:var(--muted); font-family:'JetBrains Mono',monospace">
      <span>Lowest (${fmt(minR)})</span>
      <span>Highest (${fmt(maxR)})</span>
    </div>

    <div class="la-stats">
      <div>
        <div class="la-stat-label">Population</div>
        <div class="la-stat-value">${fmt(la.population)}</div>
      </div>
      <div>
        <div class="la-stat-label">IMD score</div>
        <div class="la-stat-value">${la.imd_score.toFixed(1)}</div>
      </div>
      <div>
        <div class="la-stat-label">% aged 65+</div>
        <div class="la-stat-value">${la.pct_65_plus.toFixed(1)}%</div>
      </div>
      <div>
        <div class="la-stat-label">QOF score</div>
        <div class="la-stat-value">${la.qof_score.toFixed(1)}</div>
      </div>
      <div>
        <div class="la-stat-label">Actual rate</div>
        <div class="la-stat-value">${fmt(la.acs_admissions_per_100k)}</div>
      </div>
      <div>
        <div class="la-stat-label">Predicted rate</div>
        <div class="la-stat-value">${fmt(la.predicted_rate)}</div>
      </div>
    </div>

    <div class="note" style="margin-top:24px">
      <div class="note-title">Model verdict</div>
      <strong>${la.la_name}</strong> sits at <strong>${fmt(la.acs_admissions_per_100k)}</strong> per 100,000.
      The model predicts <strong>${fmt(la.predicted_rate)}</strong> for an LA with this deprivation, age structure, primary care quality, and region.
      Actual is <span class="pill ${residualPill}">${fmtPct(la.residual_pct)} ${residualLabel}</span>
    </div>
  `;
  div.classList.add("show");
}

// ----- Overview stats -----
const s = DATA.summary;
document.getElementById("stat-total").textContent = (s.england_total_admissions/1e6).toFixed(2)+"M";
document.getElementById("stat-rate").textContent = fmt(s.england_rate_per_100k);
document.getElementById("stat-r2").textContent = s.imd_r2_pct + "%";
document.getElementById("stat-cost").textContent = "£" + fmt(s.excess_cost_gbp/1e6) + "m";

// ----- Gradient: decile bars -----
function bar(label, val, max, cls="", suffix="") {
  const pct = (val / max) * 100;
  return `<div class="bar-row">
    <div>${label}</div>
    <div class="bar-track"><div class="bar-fill ${cls}" style="width:${pct.toFixed(1)}%"></div></div>
    <div class="num">${fmt(val)}${suffix}</div>
  </div>`;
}

const decileMax = Math.max(...DATA.by_decile.map(d => d.rate_mean));
document.getElementById("decile-bars").innerHTML = DATA.by_decile.map(d => {
  const cls = d.imd_decile <= 3 ? "green" : d.imd_decile <= 7 ? "" : d.imd_decile <= 9 ? "amber" : "red";
  return bar(`Decile ${d.imd_decile} ${d.imd_decile===1?'(least dep)':d.imd_decile===10?'(most dep)':''}`, d.rate_mean, decileMax, cls);
}).join("");

const regionMax = Math.max(...DATA.by_region.map(r => r.rate_weighted));
document.getElementById("region-bars").innerHTML = DATA.by_region.map(r => {
  const NORTH = ["North East","North West","Yorkshire and The Humber"];
  const cls = NORTH.includes(r.region) ? "amber" : "";
  return bar(r.region, r.rate_weighted, regionMax, cls);
}).join("");

// ----- Model: coefficients -----
const interpretations = {
  imd_score: "Per 1-point IMD increase",
  pct_65_plus: "Per 1pp older population",
  qof_score: "Per 1pp QOF improvement",
  urban: "Urban vs Mixed",
  rural: "Rural vs Mixed",
};
const keyCoefs = ["imd_score","pct_65_plus","qof_score","urban","rural"];
document.querySelector("#coefs-table tbody").innerHTML = keyCoefs.map(name => {
  const c = DATA.coefs.find(x => x.variable === name);
  if (!c) return "";
  const ci = `${c.ci_low.toFixed(3)}-${c.ci_high.toFixed(3)}`;
  const sig = c.p_value < 0.001 ? "<0.001" : c.p_value.toFixed(3);
  return `<tr>
    <td><strong>${name.replace(/_/g," ")}</strong></td>
    <td>${interpretations[name] || ""}</td>
    <td class="num">${c.rate_ratio.toFixed(3)}</td>
    <td class="num">${ci}</td>
    <td class="num">${sig}</td>
  </tr>`;
}).join("");

// ----- Spatial CV table -----
document.querySelector("#cv-table tbody").innerHTML = DATA.spatial_cv.map(c => {
  const r2cls = c.r2 > 0.4 ? "low" : c.r2 > 0 ? "mid" : "high";
  return `<tr>
    <td>${c.held_out_region}</td>
    <td class="num">${c.n_las}</td>
    <td class="num"><span class="pill ${r2cls}">${c.r2.toFixed(2)}</span></td>
    <td class="num">${fmt(c.mae)}</td>
  </tr>`;
}).join("");

// ----- Residuals -----
document.querySelector("#over-table tbody").innerHTML = DATA.top10_over.slice(0,10).map(r => `
  <tr>
    <td><strong>${r.LA}</strong></td>
    <td>${r.Region}</td>
    <td class="num"><span class="pill high">+${r['Excess %'].toFixed(1)}%</span></td>
  </tr>
`).join("");

document.querySelector("#under-table tbody").innerHTML = DATA.top10_under.slice(0,10).map(r => `
  <tr>
    <td><strong>${r.LA}</strong></td>
    <td>${r.Region}</td>
    <td class="num"><span class="pill low">${r['Shortfall %'].toFixed(1)}%</span></td>
  </tr>
`).join("");

// ----- Public Good -----
document.getElementById("pg-median").textContent = fmt(s.median_rate);
document.getElementById("pg-above").textContent = "154 / 309";
document.getElementById("pg-excess").textContent = fmt(s.excess_admissions/1000) + "k";
document.getElementById("pg-cost").textContent = "£" + fmt(s.excess_cost_gbp/1e6) + "m";

// Top 10 LAs by excess admissions for the savings chart
const lasWithExcess = DATA.las.map(la => {
  const median = s.median_rate;
  const excess = la.acs_admissions_per_100k > median
    ? Math.round((la.acs_admissions_per_100k - median) / 100000 * la.population)
    : 0;
  return {...la, excess};
}).filter(la => la.excess > 0).sort((a,b) => b.excess - a.excess).slice(0,10);

const savingsMax = Math.max(...lasWithExcess.map(la => la.excess));
document.getElementById("savings-bars").innerHTML = lasWithExcess.map(la => {
  return `<div class="bar-row">
    <div>${la.la_name}</div>
    <div class="bar-track"><div class="bar-fill amber" style="width:${(la.excess/savingsMax*100).toFixed(1)}%"></div></div>
    <div class="num">${fmt(la.excess)}</div>
  </div>`;
}).join("");
</script>
</body>
</html>