LUNG_CANCER/Lung_Cancer.html at main · DocNikDS/LUNG_CANCER · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>

<meta charset="utf-8">
<meta name="generator" content="quarto-1.8.25">

<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">

<meta name="author" content="Nik Danilov">
<meta name="dcterms.date" content="2025-01-01">

<title>Spatial Distribution and Molecular Characterization of Lung Cancer in a Northern Israeli Cohort</title>
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
div.column{flex: auto; overflow-x: auto;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
ul.task-list li input[type="checkbox"] {
  width: 0.8em;
  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
  vertical-align: middle;
}
</style>


<script src="Lung_Cancer_files/libs/clipboard/clipboard.min.js"></script>
<script src="Lung_Cancer_files/libs/quarto-html/quarto.js" type="module"></script>
<script src="Lung_Cancer_files/libs/quarto-html/tabsets/tabsets.js" type="module"></script>
<script src="Lung_Cancer_files/libs/quarto-html/axe/axe-check.js" type="module"></script>
<script src="Lung_Cancer_files/libs/quarto-html/popper.min.js"></script>
<script src="Lung_Cancer_files/libs/quarto-html/tippy.umd.min.js"></script>
<script src="Lung_Cancer_files/libs/quarto-html/anchor.min.js"></script>
<link href="Lung_Cancer_files/libs/quarto-html/tippy.css" rel="stylesheet">
<link href="Lung_Cancer_files/libs/quarto-html/quarto-syntax-highlighting-7b89279ff1a6dce999919e0e67d4d9ec.css" rel="stylesheet" id="quarto-text-highlighting-styles">
<script src="Lung_Cancer_files/libs/bootstrap/bootstrap.min.js"></script>
<link href="Lung_Cancer_files/libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
<link href="Lung_Cancer_files/libs/bootstrap/bootstrap-4d7f0bce1131f3e5f9547cd857cfbfc8.min.css" rel="stylesheet" append-hash="true" id="quarto-bootstrap" data-mode="light">
<script src="Lung_Cancer_files/libs/kePrint-0.0.1/kePrint.js"></script>

<link href="Lung_Cancer_files/libs/lightable-0.0.1/lightable.css" rel="stylesheet">

<link href="Lung_Cancer_files/libs/htmltools-fill-0.5.8.1/fill.css" rel="stylesheet">

<script src="Lung_Cancer_files/libs/htmlwidgets-1.6.4/htmlwidgets.js"></script>

<script src="Lung_Cancer_files/libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>

<link href="Lung_Cancer_files/libs/leaflet-1.3.1/leaflet.css" rel="stylesheet">

<script src="Lung_Cancer_files/libs/leaflet-1.3.1/leaflet.js"></script>

<link href="Lung_Cancer_files/libs/leafletfix-1.0.0/leafletfix.css" rel="stylesheet">

<script src="Lung_Cancer_files/libs/proj4-2.6.2/proj4.min.js"></script>

<script src="Lung_Cancer_files/libs/Proj4Leaflet-1.0.1/proj4leaflet.js"></script>

<link href="Lung_Cancer_files/libs/rstudio_leaflet-1.3.1/rstudio_leaflet.css" rel="stylesheet">

<script src="Lung_Cancer_files/libs/leaflet-binding-2.2.3/leaflet.js"></script>

<script src="Lung_Cancer_files/libs/leaflet-providers-2.0.0/leaflet-providers_2.0.0.js"></script>

<script src="Lung_Cancer_files/libs/leaflet-providers-plugin-2.2.3/leaflet-providers-plugin.js"></script>


</head>

<body class="quarto-light">

<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
  <nav id="TOC" role="doc-toc" class="toc-active">
    <h2 id="toc-title">Table of contents</h2>

  <ul>
  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction">Introduction</a></li>
  <li><a href="#methods" id="toc-methods" class="nav-link" data-scroll-target="#methods">Methods</a></li>
  <li><a href="#data-analysis" id="toc-data-analysis" class="nav-link" data-scroll-target="#data-analysis">Data analysis</a>
  <ul class="collapse">
  <li><a href="#cohort-demographics" id="toc-cohort-demographics" class="nav-link" data-scroll-target="#cohort-demographics">Cohort demographics</a></li>
  <li><a href="#histological-subtypes-biomarkers-and-tmb" id="toc-histological-subtypes-biomarkers-and-tmb" class="nav-link" data-scroll-target="#histological-subtypes-biomarkers-and-tmb">Histological subtypes, biomarkers, and TMB</a></li>
  <li><a href="#geographic-distribution-of-lc-cases" id="toc-geographic-distribution-of-lc-cases" class="nav-link" data-scroll-target="#geographic-distribution-of-lc-cases">Geographic distribution of LC cases</a></li>
  <li><a href="#spatial-analysis" id="toc-spatial-analysis" class="nav-link" data-scroll-target="#spatial-analysis">Spatial analysis</a></li>
  </ul></li>
  <li><a href="#discussion" id="toc-discussion" class="nav-link" data-scroll-target="#discussion">Discussion</a></li>
  <li><a href="#conclusions" id="toc-conclusions" class="nav-link" data-scroll-target="#conclusions">Conclusions</a></li>
  </ul>
</nav>
</div>
<main class="content" id="quarto-document-content">

<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title">Spatial Distribution and Molecular Characterization of Lung Cancer in a Northern Israeli Cohort</h1>
<p class="subtitle lead">A Hospital-Based Epidemiological Study</p>
</div>


<div class="quarto-title-meta-author">
  <div class="quarto-title-meta-heading">Author</div>
  <div class="quarto-title-meta-heading">Affiliation</div>

    <div class="quarto-title-meta-contents">
    <p class="author">Nik Danilov <a href="mailto:danilov.md@gmail.com" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> </p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            School of Public Health, University of Haifa | Lady Davis Carmel Medical Center, Haifa, Israel
          </p>
      </div>
  </div>

<div class="quarto-title-meta">


    <div>
    <div class="quarto-title-meta-heading">Published</div>
    <div class="quarto-title-meta-contents">
      <p class="date">2025</p>
    </div>
  </div>


  </div>


</header>


<div class="callout callout-style-simple callout-note no-icon">
<div class="callout-body d-flex">
<div class="callout-icon-container">
<i class="callout-icon no-icon"></i>
</div>
<div class="callout-body-container">
<p><em>Course instructor: Jonathan Dubnov, MD, MPH | Environmental Epidemiology, School of Public Health, University of Haifa</em></p>
</div>
</div>
</div>
<hr>
<section id="introduction" class="level2">
<h2 class="anchored" data-anchor-id="introduction">Introduction</h2>
<p>This report presents a hospital-based epidemiological analysis of lung cancer (LC) cases managed at the Lady Davis Carmel Medical Center, Haifa, Israel (2024–2025). The dataset was compiled through systematic review of anonymized electronic medical records extracted from the Ofek intra/interhospital medical informatics platform, covering 94 LC cases with their histopathological and molecular characterization.</p>
<p>Two complementary objectives guided the analysis:</p>
<ol type="1">
<li><p>To characterize the spatial distribution of LC cases across the Haifa District and Northern District and evaluate whether observed geographic patterns are explained by established confounders — particularly smoking prevalence and population size</p></li>
<li><p>To describe the molecular and histopathological profile of the cohort and examine how biomarker distributions align with internationally reported data</p></li>
</ol>
</section>
<section id="methods" class="level2">
<h2 class="anchored" data-anchor-id="methods">Methods</h2>
<p>The dataset comprised 94 anonymized LC cases extracted from the Ofek platform. After loading, variables were recoded as factors with descriptive labels to facilitate analysis and visualization. All analyses were performed in R programming language. The report was authored in Quarto to ensure full reproducibility.</p>
<p><em><a href="#tbl-dictionary" class="quarto-xref">Table&nbsp;1</a></em> summarizes all variables included in the dataset. The variables are described according to a syntactical approach, primarily distinguishing only between numeric and character. Under this approach, the functional role of a variable (continuous, discrete count, binary flag, or categorical) is determined by its analytical use rather than by its storage type.</p>
<div class="cell">
<div id="tbl-dictionary" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-dictionary-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;1: Data dictionary: variables included in the dataset
</figcaption>
<div aria-describedby="tbl-dictionary-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output-display">
<table class="table table-striped table-hover do-not-create-environment cell caption-top table-sm small">
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Variable</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Type</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Description / Coding</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">id</td>
<td style="text-align: left; width: 8em;">numeric, nominal</td>
<td style="text-align: left; width: 40em;">Anonymized case identifier (not used in analysis)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">sex</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">Biological sex: 0 = Male, 1 = Female</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">age</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">Age at the lung biopsy/surgery date (years)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">ethnic</td>
<td style="text-align: left; width: 8em;">numeric, nominal</td>
<td style="text-align: left; width: 40em;">Ethnic origin (presumed on the patient's name/surname and locality): 0 = Jewish, 1 = Arab, not otherwise specified (NOS), 2 = Arab, Druze, 3 = Arab, Bedouin</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">locality</td>
<td style="text-align: left; width: 8em;">character, nominal</td>
<td style="text-align: left; width: 40em;">Residential locality (city, town, or village)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">latitude</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">Locality geographic latitude (decimal degrees)</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">longitude</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">Locality geographic longitude (decimal degrees)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">loc_popul</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">Locality population size (Israeli Central Bureau of Statistics, 2023)</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">district</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">Israeli administrative district: 0 = Haifa District, 1 = Northern District</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">smoking</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">Smoking status: 0 = Non-smoker (never smoked or quit &gt; 10 years before the lung biopsy/surgery date), 1 = Smoker (current smoker or quit ≤ 10 years before the lung biopsy/surgery date)</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">bx_date</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">The lung biopsy/surgery date</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">dx</td>
<td style="text-align: left; width: 8em;">numeric, nominal</td>
<td style="text-align: left; width: 40em;">Histological diagnosis, the LC subtype: 0 = adenocarcinoma (ACA), NOS, 1 = Mucinous ACA, 2 = Squamous cell carcinoma (SCC), 3 = neuroendocrine tumor (NET): low-grade (typical carcinoid) or intermediate-grade (atypical carcinoid) neuroendocrine neoplasm, 4 = neuroendocrine carcinoma (NEC): high-grade neuroendocrine neoplasm (small cell or large cell carcinoma)</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">pdl1</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">PD-L1 expression measured by tumor proportion score (TPS) - the percentage of viable tumor cells expressing PD-L1 on their surface: 0 = Weak (TPS 1–49%), 1 = Strong (TPS ≥ 50%)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">met</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">MET gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">egfr</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">EGFR gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">braf</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">BRAF gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">kras</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">KRAS gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">erbb2</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">ERBB2 (HER2) gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">alk</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">ALK gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">ros1</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">ROS1 gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">ntrk</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">NTRK1/NTRK2/NTRK3 gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">ret</td>
<td style="text-align: left; width: 8em;">numeric, binary</td>
<td style="text-align: left; width: 40em;">RET gene mutation status: 0 = no mutation, 1 = mutation detected</td>
</tr>
<tr class="odd">
<td style="text-align: left; width: 8em; font-weight: bold;">msi</td>
<td style="text-align: left; width: 8em;">numeric, ordinal</td>
<td style="text-align: left; width: 40em;">Microsatellite instability (MSI) status of the tumor - instability of short tandem DNA repeats (microsatellites) of the tumor cells as a result of abnormal function of ≥ 1 mismatch repair genes (MLH1, PMS2, MSH2, and MSH6): 0 = microsatellite stable (MSS), 1 = low microsatellite instability (MSI-L), 2 = high microsatellite instability (MSI-H)</td>
</tr>
<tr class="even">
<td style="text-align: left; width: 8em; font-weight: bold;">tmb</td>
<td style="text-align: left; width: 8em;">numeric, continuous</td>
<td style="text-align: left; width: 40em;">Tumor mutational burden (TMB) - number of non-inherited mutations per million tumor DNA bases</td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
</section>
<section id="data-analysis" class="level2">
<h2 class="anchored" data-anchor-id="data-analysis">Data analysis</h2>
<section id="cohort-demographics" class="level3">
<h3 class="anchored" data-anchor-id="cohort-demographics">Cohort demographics</h3>
<p>The study cohort comprised 94 LC patients with a median age of 72 years (range: 42–91). The sex distribution was male-predominant (55.3% males, 44.7% females). Smoking status was nearly balanced: 51.1% non-smokers and 48.9% smokers. Geographically, 39.4% of patients resided in the Haifa District and 60.6% in the Northern District. The ethnic composition was predominantly Jewish (68.1%), followed by Arab (25.5%), Druze (5.3%), and Bedouin (1.1%).</p>
<p>Full demographic characteristics are presented in <em><a href="#tbl-demographics" class="quarto-xref">Table&nbsp;2</a></em> and <em><a href="#fig-demographics" class="quarto-xref">Figure&nbsp;1</a></em>.</p>
<div class="cell">
<div id="tbl-demographics" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-demographics-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;2: Demographic characteristics of the study cohort
</figcaption>
<div aria-describedby="tbl-demographics-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output-display">
<table class="table table-striped table-bordered do-not-create-environment cell caption-top table-sm small">
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Variable</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Subgroup</th>
<th style="text-align: center;" data-quarto-table-cell-role="th">N</th>
<th style="text-align: center;" data-quarto-table-cell-role="th">%</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Total patients</td>
<td style="text-align: left;"></td>
<td style="text-align: center;">94</td>
<td style="text-align: center;">100%</td>
</tr>
<tr class="even">
<td style="text-align: left;">Age, years: median (range)</td>
<td style="text-align: left;"></td>
<td style="text-align: center;">72 (42–91)</td>
<td style="text-align: center;">—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Sex</td>
<td style="text-align: left;">Female</td>
<td style="text-align: center;">42</td>
<td style="text-align: center;">44.7%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">Male</td>
<td style="text-align: center;">52</td>
<td style="text-align: center;">55.3%</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Smoking</td>
<td style="text-align: left;">Non-smoker</td>
<td style="text-align: center;">48</td>
<td style="text-align: center;">51.1%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">Smoker</td>
<td style="text-align: center;">46</td>
<td style="text-align: center;">48.9%</td>
</tr>
<tr class="odd">
<td style="text-align: left;">District</td>
<td style="text-align: left;">Haifa</td>
<td style="text-align: center;">37</td>
<td style="text-align: center;">39.4%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">Northern</td>
<td style="text-align: center;">57</td>
<td style="text-align: center;">60.6%</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Ethnicity</td>
<td style="text-align: left;">Jewish</td>
<td style="text-align: center;">64</td>
<td style="text-align: center;">68.1%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">Arab</td>
<td style="text-align: center;">24</td>
<td style="text-align: center;">25.5%</td>
</tr>
<tr class="odd">
<td style="text-align: left;"></td>
<td style="text-align: left;">Druze</td>
<td style="text-align: center;">5</td>
<td style="text-align: center;">5.3%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">Bedouin</td>
<td style="text-align: center;">1</td>
<td style="text-align: center;">1.1%</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Histology</td>
<td style="text-align: left;">ACA</td>
<td style="text-align: center;">63</td>
<td style="text-align: center;">67%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">SCC</td>
<td style="text-align: center;">25</td>
<td style="text-align: center;">26.6%</td>
</tr>
<tr class="odd">
<td style="text-align: left;"></td>
<td style="text-align: left;">MucACA</td>
<td style="text-align: center;">4</td>
<td style="text-align: center;">4.3%</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;">NEC</td>
<td style="text-align: center;">2</td>
<td style="text-align: center;">2.1%</td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
<div class="cell">
<div class="cell-output-display">
<div id="fig-demographics" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-demographics-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="Lung_Cancer_files/figure-html/fig-demographics-1.png" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-demographics-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;1: Sex, smoking status, district of residence, and ethnic distribution of the study cohort
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="histological-subtypes-biomarkers-and-tmb" class="level3">
<h3 class="anchored" data-anchor-id="histological-subtypes-biomarkers-and-tmb">Histological subtypes, biomarkers, and TMB</h3>
<p>ACA was the predominant histological subtype, accounting for 67.0% of cases, followed by SCC (26.6%), MucACA (4.3%), and NEC (2.1%). No carcinoid tumors (NETs) were identified in this cohort.</p>
<p>Biomarker positivity was concentrated in ACA: EGFR, BRAF, and ROS1 alterations occurred exclusively in ACAs, while KRAS and PD-L1 positivity were also predominantly found in this subtype. SCC cases showed lower rates of PD-L1 (25.0%) and KRAS (8.7%) positivity.</p>
<p>Mean TMB differed across histological subtypes, ranging from 6.7 mutations/Mb in ACA to 16.1 mutations/Mb in NEC — consistent with the known hypermutated phenotype of the latter.</p>
<p>Biomarker distribution and mean TMB by histological subtype are shown in <em><a href="#fig-biomarkers" class="quarto-xref">Figure&nbsp;2</a></em>.</p>
<div class="cell">
<div class="cell-output-display">
<div id="fig-biomarkers" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-biomarkers-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="Lung_Cancer_files/figure-html/fig-biomarkers-1.png" class="img-fluid figure-img" width="960">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-biomarkers-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;2: Histopahological diagnoses with biomarker distribution and mean TMB
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="geographic-distribution-of-lc-cases" class="level3">
<h3 class="anchored" data-anchor-id="geographic-distribution-of-lc-cases">Geographic distribution of LC cases</h3>
<p>Cases were distributed across more than 40 localities within the Haifa District and Northern District. The largest concentration resided in the city of Haifa (18.1% of the cohort), followed by Acre (6.4%), Kiryat Ata (6.4%), and Nazareth (4.3%). The remaining cases were dispersed across smaller towns and villages, each contributing ≤3.2% of the total (<em><a href="#fig-locality-bar" class="quarto-xref">Figure&nbsp;3</a></em>).</p>
<div class="cell">
<div class="cell-output-display">
<div id="fig-locality-bar" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-locality-bar-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="Lung_Cancer_files/figure-html/fig-locality-bar-1.png" class="img-fluid figure-img" width="864">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-locality-bar-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;3: Geographic distribution of LC patients by locality of residence. ‘Other’ bars represent localities with a single case each, grouped by histopathological diagnosis.
</figcaption>
</figure>
</div>
</div>
</div>
<p>Histopathological composition varied between localities. Haifa cases were predominantly ACA (64.7%) with a substantial SCC fraction (35.3%). Acre cases split evenly between ACA and SCC, while Nazareth showed a more mixed profile including ACA, SCC, and NEC.</p>
<p>The bubble map (<em><a href="#fig-bubble-map" class="quarto-xref">Figure&nbsp;4</a></em>) provides a geographic overview of absolute case counts. The city of Haifa emerges as the dominant cluster, with additional concentrations along the coastal plain (Acre, Nahariyya, Kiryat Ata) and in inland centers (Nazareth, Nof HaGalil, Afula). However, absolute case counts reflect both underlying disease risk and population size — larger cities generate more cases simply because more people live there. Population adjustment, applied in the subsequent spatial analyses, is therefore essential before interpreting geographic patterns as evidence of differential risk.</p>
<div class="cell">
<div id="fig-bubble-map" class="cell-output-display quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-bubble-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="leaflet html-widget html-fill-item" id="htmlwidget-b812f6ea16b832cdfb32" style="width:100%;height:464px;"></div>
<script type="application/json" data-for="htmlwidget-b812f6ea16b832cdfb32">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addCircleMarkers","args":[[32.9575,32.926111111111,32.607777777778,32.719722222222,32.496111111111,32.965278,32.693055555556,32.862411111111,32.936388888889,32.656197222222,32.902597222222,33.033227777778,33.049444444444,32.556397222222,32.45,32.8191666666667,32.6825,32.933372222222,33.020897222222,32.816666666667,32.9,32.9925,32.8,32.833055555556,32.836944444444,33.2075,32.833055555556,32.963827777778,32.963055555556,32.678611111111,33.003611111111,32.931944444444,32.702102777778,32.7711111111111,32.716666666667,32.93165,32.866666666667,32.888888888889,32.7219444444444,33.165397222222,32.805555555556,32.851147222222,32.789722222222,32.766666666667,32.965977777778,32.776936111111,32.686944444444,32.933333333333,32.836944444444],[35.172777777778,35.083888888889,35.289722222222,35.144166666667,35.498888888889,35.3794,35.049444444444,35.371169444444,35.271944444444,35.417097222222,35.096297222222,35.322727777778,35.305833333333,35.076997222222,34.916666666667,34.9991666666667,35.324444444444,35.141313888889,35.149,35.266666666667,35.283333333333,35.690555555556,35.1,35.066388888889,35.0775,35.569722222222,35.066388888889,35.302308333333,35.0975,35.244444444444,35.0925,35.3175,35.303333,35.0394444444444,35.333333333333,35.319586111111,35.3,35.236944444444,35.1116666666667,35.660697222222,35.169444444444,35.207108333333,35.524722222222,34.966666666667,35.593205555556,35.375675,35.275277777778,35.183333333333,35.273611111111],[4,9.797958971132712,5.656854249492381,4,4,4,4,4,4,4,4,4,4,4,4,16.49242250247064,4,4,4,4,5.656854249492381,4,9.797958971132712,6.928203230275509,5.656854249492381,5.656854249492381,5.656854249492381,4,4,5.656854249492381,6.928203230275509,4,8,5.656854249492381,6.928203230275509,4,5.656854249492381,4,4,4,4,4,5.656854249492381,5.656854249492381,4,4,4,4,4],null,null,{"interactive":true,"className":"","stroke":true,"color":"steelblue","weight":1,"opacity":0.5,"fill":true,"fillColor":"steelblue","fillOpacity":0.6},null,null,["<b>Abu Snan<\/b><br>Cases: 1","<b>Acre<\/b><br>Cases: 6","<b>Afula<\/b><br>Cases: 2","<b>Alonim<\/b><br>Cases: 1","<b>Beit She'an<\/b><br>Cases: 1","<b>Beitegen<\/b><br>Cases: 1","<b>Daliyat al-Karmel<\/b><br>Cases: 1","<b>Deir Hanna<\/b><br>Cases: 1","<b>Deir al-Asad<\/b><br>Cases: 1","<b>Ein Dor<\/b><br>Cases: 1","<b>Ein HaMifratz<\/b><br>Cases: 1","<b>Elkosh<\/b><br>Cases: 1","<b>Fassuta<\/b><br>Cases: 1","<b>Gal'ed<\/b><br>Cases: 1","<b>Hadera<\/b><br>Cases: 1","<b>Haifa<\/b><br>Cases: 17","<b>Iksal<\/b><br>Cases: 1","<b>Jadeidi-Makr<\/b><br>Cases: 1","<b>Kabri<\/b><br>Cases: 1","<b>Kafr Manda<\/b><br>Cases: 1","<b>Karmiel<\/b><br>Cases: 2","<b>Katzrin<\/b><br>Cases: 1","<b>Kiryat Ata<\/b><br>Cases: 6","<b>Kiryat Bialik<\/b><br>Cases: 3","<b>Kiryat Motzkin<\/b><br>Cases: 2","<b>Kiryat Shmona<\/b><br>Cases: 2","<b>Kiryat Yam<\/b><br>Cases: 2","<b>Kisra-Sumei<\/b><br>Cases: 1","<b>Lohamei HaGeta'ot<\/b><br>Cases: 1","<b>Migdal HaEmek<\/b><br>Cases: 2","<b>Nahariya<\/b><br>Cases: 3","<b>Nahf<\/b><br>Cases: 1","<b>Nazareth<\/b><br>Cases: 4","<b>Nesher<\/b><br>Cases: 2","<b>Nof HaGalil<\/b><br>Cases: 3","<b>Sajur<\/b><br>Cases: 1","<b>Sakhnin<\/b><br>Cases: 2","<b>Sha'ab<\/b><br>Cases: 1","<b>Sha'ar HaAmakim<\/b><br>Cases: 1","<b>Shamir<\/b><br>Cases: 1","<b>Shefa-Amr<\/b><br>Cases: 1","<b>Tamra<\/b><br>Cases: 1","<b>Tiberias<\/b><br>Cases: 2","<b>Tirat Carmel<\/b><br>Cases: 2","<b>Tuba-Zangariyye<\/b><br>Cases: 1","<b>Tur'an<\/b><br>Cases: 1","<b>Yafa an-Naseriyye<\/b><br>Cases: 1","<b>Yarka<\/b><br>Cases: 1","<b>Yodfat<\/b><br>Cases: 1"],null,["Abu Snan","Acre","Afula","Alonim","Beit She'an","Beitegen","Daliyat al-Karmel","Deir Hanna","Deir al-Asad","Ein Dor","Ein HaMifratz","Elkosh","Fassuta","Gal'ed","Hadera","Haifa","Iksal","Jadeidi-Makr","Kabri","Kafr Manda","Karmiel","Katzrin","Kiryat Ata","Kiryat Bialik","Kiryat Motzkin","Kiryat Shmona","Kiryat Yam","Kisra-Sumei","Lohamei HaGeta'ot","Migdal HaEmek","Nahariya","Nahf","Nazareth","Nesher","Nof HaGalil","Sajur","Sakhnin","Sha'ab","Sha'ar HaAmakim","Shamir","Shefa-Amr","Tamra","Tiberias","Tirat Carmel","Tuba-Zangariyye","Tur'an","Yafa an-Naseriyye","Yarka","Yodfat"],{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addLegend","args":[{"colors":["steelblue"],"labels":["LC cases"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"bottomright","type":"unknown","title":"Cases (n)","extra":null,"layerId":null,"className":"info legend","group":null}]}],"setView":[[32.8,35.1],8,[]],"limits":{"lat":[32.45,33.2075],"lng":[34.916666666667,35.690555555556]}},"evals":[],"jsHooks":[]}</script>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-bubble-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;4: Interactive bubble map of LC cases across Northern Israel by locality of residence. Bubble size corresponds to number of cases. Click to interact.
</figcaption>
</figure>
</div>
</div>
</section>
<section id="spatial-analysis" class="level3">
<h3 class="anchored" data-anchor-id="spatial-analysis">Spatial analysis</h3>
<section id="kernel-density-estimation-kde" class="level4">
<h4 class="anchored" data-anchor-id="kernel-density-estimation-kde">Kernel density estimation (KDE)</h4>
<p>To explore geographic heterogeneity beyond absolute counts, KDE was applied in two forms: unadjusted (weighted by absolute case counts) and population-adjusted (weighted by cases per 100,000 locality population).</p>
<p>The unadjusted map (<em><a href="#fig-kde-map-unadj" class="quarto-xref">Figure&nbsp;5</a></em>) confirms Haifa’s dominance — a pattern driven largely by its large population rather than elevated relative risk.</p>
<div class="cell">
<div id="fig-kde-map-unadj" class="cell-output-display quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-kde-map-unadj-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="leaflet html-widget html-fill-item" id="htmlwidget-030597d986d984f9875d" style="width:100%;height:464px;"></div>
<script type="application/json" data-for="htmlwidget-030597d986d984f9875d">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addCircleMarkers","args":[[32.9575,32.926111111111,32.607777777778,32.719722222222,32.496111111111,32.965278,32.693055555556,32.862411111111,32.936388888889,32.656197222222,32.902597222222,33.033227777778,33.049444444444,32.556397222222,32.45,32.8191666666667,32.6825,32.933372222222,33.020897222222,32.816666666667,32.9,32.9925,32.8,32.833055555556,32.836944444444,33.2075,32.833055555556,32.963827777778,32.963055555556,32.678611111111,33.003611111111,32.931944444444,32.702102777778,32.7711111111111,32.716666666667,32.93165,32.866666666667,32.888888888889,32.7219444444444,33.165397222222,32.805555555556,32.851147222222,32.789722222222,32.766666666667,32.965977777778,32.776936111111,32.686944444444,32.933333333333,32.836944444444],[35.172777777778,35.083888888889,35.289722222222,35.144166666667,35.498888888889,35.3794,35.049444444444,35.371169444444,35.271944444444,35.417097222222,35.096297222222,35.322727777778,35.305833333333,35.076997222222,34.916666666667,34.9991666666667,35.324444444444,35.141313888889,35.149,35.266666666667,35.283333333333,35.690555555556,35.1,35.066388888889,35.0775,35.569722222222,35.066388888889,35.302308333333,35.0975,35.244444444444,35.0925,35.3175,35.303333,35.0394444444444,35.333333333333,35.319586111111,35.3,35.236944444444,35.1116666666667,35.660697222222,35.169444444444,35.207108333333,35.524722222222,34.966666666667,35.593205555556,35.375675,35.275277777778,35.183333333333,35.273611111111],[10,25.625,13.125,10,10,10,10,10,10,10,10,10,10,10,10,60,10,10,10,10,13.125,10,25.625,16.25,13.125,13.125,13.125,10,10,13.125,16.25,10,19.375,13.125,16.25,10,13.125,10,10,10,10,10,13.125,13.125,10,10,10,10,10],null,null,{"interactive":true,"className":"","stroke":false,"color":"#03F","weight":5,"opacity":0.5,"fill":true,"fillColor":"red","fillOpacity":[0.2,0.35625,0.23125,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.7,0.2,0.2,0.2,0.2,0.23125,0.2,0.35625,0.2625,0.23125,0.23125,0.23125,0.2,0.2,0.23125,0.2625,0.2,0.29375,0.23125,0.2625,0.2,0.23125,0.2,0.2,0.2,0.2,0.2,0.23125,0.23125,0.2,0.2,0.2,0.2,0.2]},null,null,["<b>Abu Snan<\/b><br>Cases: 1","<b>Acre<\/b><br>Cases: 6","<b>Afula<\/b><br>Cases: 2","<b>Alonim<\/b><br>Cases: 1","<b>Beit She'an<\/b><br>Cases: 1","<b>Beitegen<\/b><br>Cases: 1","<b>Daliyat al-Karmel<\/b><br>Cases: 1","<b>Deir Hanna<\/b><br>Cases: 1","<b>Deir al-Asad<\/b><br>Cases: 1","<b>Ein Dor<\/b><br>Cases: 1","<b>Ein HaMifratz<\/b><br>Cases: 1","<b>Elkosh<\/b><br>Cases: 1","<b>Fassuta<\/b><br>Cases: 1","<b>Gal'ed<\/b><br>Cases: 1","<b>Hadera<\/b><br>Cases: 1","<b>Haifa<\/b><br>Cases: 17","<b>Iksal<\/b><br>Cases: 1","<b>Jadeidi-Makr<\/b><br>Cases: 1","<b>Kabri<\/b><br>Cases: 1","<b>Kafr Manda<\/b><br>Cases: 1","<b>Karmiel<\/b><br>Cases: 2","<b>Katzrin<\/b><br>Cases: 1","<b>Kiryat Ata<\/b><br>Cases: 6","<b>Kiryat Bialik<\/b><br>Cases: 3","<b>Kiryat Motzkin<\/b><br>Cases: 2","<b>Kiryat Shmona<\/b><br>Cases: 2","<b>Kiryat Yam<\/b><br>Cases: 2","<b>Kisra-Sumei<\/b><br>Cases: 1","<b>Lohamei HaGeta'ot<\/b><br>Cases: 1","<b>Migdal HaEmek<\/b><br>Cases: 2","<b>Nahariya<\/b><br>Cases: 3","<b>Nahf<\/b><br>Cases: 1","<b>Nazareth<\/b><br>Cases: 4","<b>Nesher<\/b><br>Cases: 2","<b>Nof HaGalil<\/b><br>Cases: 3","<b>Sajur<\/b><br>Cases: 1","<b>Sakhnin<\/b><br>Cases: 2","<b>Sha'ab<\/b><br>Cases: 1","<b>Sha'ar HaAmakim<\/b><br>Cases: 1","<b>Shamir<\/b><br>Cases: 1","<b>Shefa-Amr<\/b><br>Cases: 1","<b>Tamra<\/b><br>Cases: 1","<b>Tiberias<\/b><br>Cases: 2","<b>Tirat Carmel<\/b><br>Cases: 2","<b>Tuba-Zangariyye<\/b><br>Cases: 1","<b>Tur'an<\/b><br>Cases: 1","<b>Yafa an-Naseriyye<\/b><br>Cases: 1","<b>Yarka<\/b><br>Cases: 1","<b>Yodfat<\/b><br>Cases: 1"],null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addControl","args":["<b>Unadjusted<\/b><br>(absolute counts)","topright",null,"info legend"]}],"setView":[[32.5,35.1],8,[]],"limits":{"lat":[32.45,33.2075],"lng":[34.916666666667,35.690555555556]}},"evals":[],"jsHooks":[]}</script>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-kde-map-unadj-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;5: Unadjusted (absolute counts) kernel density heatmap of LC cases across Northern Israel. Bubble size and opacity reflect case density. Click to interact.
</figcaption>
</figure>
</div>
</div>
<p>After population adjustment (<em><a href="#fig-kde-map-adj" class="quarto-xref">Figure&nbsp;6</a></em>), Haifa’s prominence diminishes substantially. Instead, small inland localities emerge with elevated relative intensity, most notably Elkosh (1 case in a population of 267, yielding 374.5 per 100,000). The adjusted map thus provides a more valid basis for assessing spatial risk gradients than raw case counts alone.</p>
<div class="cell">
<div id="fig-kde-map-adj" class="cell-output-display quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-kde-map-adj-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="leaflet html-widget html-fill-item" id="htmlwidget-8d262844154d6d9e5a9c" style="width:100%;height:464px;"></div>
<script type="application/json" data-for="htmlwidget-8d262844154d6d9e5a9c">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addCircleMarkers","args":[[32.9575,32.926111111111,32.607777777778,32.719722222222,32.496111111111,32.965278,32.693055555556,32.862411111111,32.936388888889,32.656197222222,32.902597222222,33.033227777778,33.049444444444,32.556397222222,32.45,32.8191666666667,32.6825,32.933372222222,33.020897222222,32.816666666667,32.9,32.9925,32.8,32.833055555556,32.836944444444,33.2075,32.833055555556,32.963827777778,32.963055555556,32.678611111111,33.003611111111,32.931944444444,32.702102777778,32.7711111111111,32.716666666667,32.93165,32.866666666667,32.888888888889,32.7219444444444,33.165397222222,32.805555555556,32.851147222222,32.789722222222,32.766666666667,32.965977777778,32.776936111111,32.686944444444,32.933333333333,32.836944444444],[35.172777777778,35.083888888889,35.289722222222,35.144166666667,35.498888888889,35.3794,35.049444444444,35.371169444444,35.271944444444,35.417097222222,35.096297222222,35.322727777778,35.305833333333,35.076997222222,34.916666666667,34.9991666666667,35.324444444444,35.141313888889,35.149,35.266666666667,35.283333333333,35.690555555556,35.1,35.066388888889,35.0775,35.569722222222,35.066388888889,35.302308333333,35.0975,35.244444444444,35.0925,35.3175,35.303333,35.0394444444444,35.333333333333,35.319586111111,35.3,35.236944444444,35.1116666666667,35.660697222222,35.169444444444,35.207108333333,35.524722222222,34.966666666667,35.593205555556,35.375675,35.275277777778,35.183333333333,35.273611111111],[10.81482604906416,11.37778689552968,10.28913559620037,28.1331307210017,10.53559025361206,10.96388372341974,10.61040527171138,11.13450335520393,10.95066013709431,21.33309556974123,24.99721010118436,60,13.82606851601367,32.55849238515182,10,10.63733353750198,10.76806807281036,10.53887053908708,22.16433299203953,10.4901755724631,10.42455697208345,11.53884220604507,11.16702680366073,10.73963915472148,10.395920325071,10.97825480533838,10.50824254326803,11.32668737361208,23.55917482018442,10.81235579022072,10.46236157235987,10.84319884361437,10.56801770414482,11.05069734718815,10.75538804598043,13.01115119123454,10.68085436085238,11.62526288349437,26.73040075065801,25.94123574047922,10.18296357698845,10.24383451128102,10.39463425197749,10.7138397671736,11.7575197091952,10.78310803953661,10.59407087553953,10.68365657372714,23.25811517587184],null,null,{"interactive":true,"className":"","stroke":false,"color":"#03F","weight":5,"opacity":0.5,"fill":true,"fillColor":"red","fillOpacity":[0.2081482604906416,0.2137778689552968,0.2028913559620037,0.381331307210017,0.2053559025361206,0.2096388372341975,0.2061040527171138,0.2113450335520393,0.2095066013709431,0.3133309556974123,0.3499721010118436,0.7,0.2382606851601367,0.4255849238515181,0.2,0.2063733353750198,0.2076806807281036,0.2053887053908708,0.3216433299203953,0.204901755724631,0.2042455697208345,0.2153884220604508,0.2116702680366073,0.2073963915472148,0.20395920325071,0.2097825480533838,0.2050824254326803,0.2132668737361209,0.3355917482018441,0.2081235579022072,0.2046236157235987,0.2084319884361437,0.2056801770414482,0.2105069734718815,0.2075538804598043,0.2301115119123455,0.2068085436085238,0.2162526288349437,0.3673040075065801,0.3594123574047922,0.2018296357698845,0.2024383451128102,0.2039463425197749,0.2071383976717361,0.217575197091952,0.2078310803953661,0.2059407087553953,0.2068365657372714,0.3325811517587184]},null,null,["<b>Abu Snan<\/b><br>Adjusted intensity: 7","<b>Acre<\/b><br>Adjusted intensity: 11.2","<b>Afula<\/b><br>Adjusted intensity: 3.1","<b>Alonim<\/b><br>Adjusted intensity: 136.4","<b>Beit She'an<\/b><br>Adjusted intensity: 4.9","<b>Beitegen<\/b><br>Adjusted intensity: 8.1","<b>Daliyat al-Karmel<\/b><br>Adjusted intensity: 5.5","<b>Deir Hanna<\/b><br>Adjusted intensity: 9.4","<b>Deir al-Asad<\/b><br>Adjusted intensity: 8","<b>Ein Dor<\/b><br>Adjusted intensity: 85.6","<b>Ein HaMifratz<\/b><br>Adjusted intensity: 113","<b>Elkosh<\/b><br>Adjusted intensity: 374.5","<b>Fassuta<\/b><br>Adjusted intensity: 29.5","<b>Gal'ed<\/b><br>Adjusted intensity: 169.5","<b>Hadera<\/b><br>Adjusted intensity: 0.9","<b>Haifa<\/b><br>Adjusted intensity: 5.7","<b>Iksal<\/b><br>Adjusted intensity: 6.7","<b>Jadeidi-Makr<\/b><br>Adjusted intensity: 5","<b>Kabri<\/b><br>Adjusted intensity: 91.8","<b>Kafr Manda<\/b><br>Adjusted intensity: 4.6","<b>Karmiel<\/b><br>Adjusted intensity: 4.1","<b>Katzrin<\/b><br>Adjusted intensity: 12.4","<b>Kiryat Ata<\/b><br>Adjusted intensity: 9.7","<b>Kiryat Bialik<\/b><br>Adjusted intensity: 6.5","<b>Kiryat Motzkin<\/b><br>Adjusted intensity: 3.9","<b>Kiryat Shmona<\/b><br>Adjusted intensity: 8.2","<b>Kiryat Yam<\/b><br>Adjusted intensity: 4.7","<b>Kisra-Sumei<\/b><br>Adjusted intensity: 10.8","<b>Lohamei HaGeta'ot<\/b><br>Adjusted intensity: 102.2","<b>Migdal HaEmek<\/b><br>Adjusted intensity: 7","<b>Nahariya<\/b><br>Adjusted intensity: 4.4","<b>Nahf<\/b><br>Adjusted intensity: 7.2","<b>Nazareth<\/b><br>Adjusted intensity: 5.2","<b>Nesher<\/b><br>Adjusted intensity: 8.8","<b>Nof HaGalil<\/b><br>Adjusted intensity: 6.6","<b>Sajur<\/b><br>Adjusted intensity: 23.4","<b>Sakhnin<\/b><br>Adjusted intensity: 6","<b>Sha'ab<\/b><br>Adjusted intensity: 13.1","<b>Sha'ar HaAmakim<\/b><br>Adjusted intensity: 125.9","<b>Shamir<\/b><br>Adjusted intensity: 120","<b>Shefa-Amr<\/b><br>Adjusted intensity: 2.3","<b>Tamra<\/b><br>Adjusted intensity: 2.8","<b>Tiberias<\/b><br>Adjusted intensity: 3.9","<b>Tirat Carmel<\/b><br>Adjusted intensity: 6.3","<b>Tuba-Zangariyye<\/b><br>Adjusted intensity: 14.1","<b>Tur'an<\/b><br>Adjusted intensity: 6.8","<b>Yafa an-Naseriyye<\/b><br>Adjusted intensity: 5.4","<b>Yarka<\/b><br>Adjusted intensity: 6","<b>Yodfat<\/b><br>Adjusted intensity: 100"],null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addControl","args":["<b>Population-adjusted<\/b><br>(relative risk intensity)","topright",null,"info legend"]}],"setView":[[32.5,35.1],8,[]],"limits":{"lat":[32.45,33.2075],"lng":[34.916666666667,35.690555555556]}},"evals":[],"jsHooks":[]}</script>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-kde-map-adj-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;6: Population-adjusted (relative risk intensity) kernel density heatmap of LC cases across Northern Israel. Bubble size and opacity reflect case density. Click to interact.
</figcaption>
</figure>
</div>
</div>
</section>
<section id="global-spatial-autocorrelation" class="level4">
<h4 class="anchored" data-anchor-id="global-spatial-autocorrelation">Global spatial autocorrelation</h4>
<p>Global spatial autocorrelation was assessed using Moran’s I with a k = 4 nearest-neighbor weights matrix. The result was Moran’s I = −0.008 (permutation test p = 0.373), indicating no statistically significant global clustering of LC incidence across localities (<em><a href="#fig-moran" class="quarto-xref">Figure&nbsp;7</a></em>). This finding is consistent with the overall conclusion that apparent geographic gradients in crude case maps are attributable to population size and confounding rather than true spatial clustering of risk.</p>
<div class="cell">
<div class="cell-output-display">
<div id="fig-moran" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-moran-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="Lung_Cancer_files/figure-html/fig-moran-1.png" class="img-fluid figure-img" width="672">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-moran-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;7: Moran scatterplot of LC incidence across Northern Israel. Each point represents a locality. Positive spatial autocorrelation is indicated by points clustering in the upper-right (high–high) and lower-left (low–low) quadrants.
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="local-indicators-of-spatial-association" class="level4">
<h4 class="anchored" data-anchor-id="local-indicators-of-spatial-association">Local indicators of spatial association</h4>
<p>LISA analysis identified three statistically significant Low–High outliers (p &lt; 0.05): Beitegen, Fassuta, and Kiryat Shmona — localities with relatively low incidence surrounded by higher-incidence neighbors (<em><a href="#fig-lisa" class="quarto-xref">Figure&nbsp;8</a></em>, <em><a href="#tbl-lisa" class="quarto-xref">Table&nbsp;3</a></em>). No High–High hotspots or High–Low outliers reached statistical significance. This absence of significant local clustering corroborates the global Moran’s I result (I = −0.008, p = 0.373) and is consistent with the conclusion that apparent geographic gradients in crude case maps are attributable to population size distribution and smoking prevalence rather than true spatial clustering of risk.</p>
<div class="cell">
<div id="fig-lisa" class="cell-output-display quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-lisa-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="leaflet html-widget html-fill-item" id="htmlwidget-0e2fbed64acdf8d74086" style="width:100%;height:464px;"></div>
<script type="application/json" data-for="htmlwidget-0e2fbed64acdf8d74086">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addCircleMarkers","args":[[32.9575,32.926111111111,32.607777777778,32.719722222222,32.496111111111,32.965278,32.693055555556,32.862411111111,32.936388888889,32.656197222222,32.902597222222,33.033227777778,33.049444444444,32.556397222222,32.45,32.8191666666667,32.6825,32.933372222222,33.020897222222,32.816666666667,32.9,32.9925,32.8,32.833055555556,32.836944444444,33.2075,32.833055555556,32.963827777778,32.963055555556,32.678611111111,33.003611111111,32.931944444444,32.702102777778,32.7711111111111,32.716666666667,32.93165,32.866666666667,32.888888888889,32.7219444444444,33.165397222222,32.805555555556,32.851147222222,32.789722222222,32.766666666667,32.965977777778,32.776936111111,32.686944444444,32.933333333333,32.836944444444],[35.172777777778,35.083888888889,35.289722222222,35.144166666667,35.498888888889,35.3794,35.049444444444,35.371169444444,35.271944444444,35.417097222222,35.096297222222,35.322727777778,35.305833333333,35.076997222222,34.916666666667,34.9991666666667,35.324444444444,35.141313888889,35.149,35.266666666667,35.283333333333,35.690555555556,35.1,35.066388888889,35.0775,35.569722222222,35.066388888889,35.302308333333,35.0975,35.244444444444,35.0925,35.3175,35.303333,35.0394444444444,35.333333333333,35.319586111111,35.3,35.236944444444,35.1116666666667,35.660697222222,35.169444444444,35.207108333333,35.524722222222,34.966666666667,35.593205555556,35.375675,35.275277777778,35.183333333333,35.273611111111],8,null,null,{"interactive":true,"className":"","stroke":false,"color":["#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC"],"weight":5,"opacity":0.5,"fill":true,"fillColor":["#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#91BFDB","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC","#CCCCCC"],"fillOpacity":0.8},null,null,["<b>Abu Snan<\/b><br>Incidence: 7 per 100,000<br>LISA p = 0.614<br>Cluster: Not significant","<b>Acre<\/b><br>Incidence: 11.2 per 100,000<br>LISA p = 0.58<br>Cluster: Not significant","<b>Afula<\/b><br>Incidence: 3.1 per 100,000<br>LISA p = 0.347<br>Cluster: Not significant","<b>Alonim<\/b><br>Incidence: 136.4 per 100,000<br>LISA p = 0.92<br>Cluster: Not significant","<b>Beit She'an<\/b><br>Incidence: 4.9 per 100,000<br>LISA p = 0.762<br>Cluster: Not significant","<b>Beitegen<\/b><br>Incidence: 8.1 per 100,000<br>LISA p = 0.049<br>Cluster: Low–High outlier","<b>Daliyat al-Karmel<\/b><br>Incidence: 5.5 per 100,000<br>LISA p = 0.314<br>Cluster: Not significant","<b>Deir Hanna<\/b><br>Incidence: 9.4 per 100,000<br>LISA p = 0.463<br>Cluster: Not significant","<b>Deir al-Asad<\/b><br>Incidence: 8 per 100,000<br>LISA p = 0.462<br>Cluster: Not significant","<b>Ein Dor<\/b><br>Incidence: 85.6 per 100,000<br>LISA p = 0.388<br>Cluster: Not significant","<b>Ein HaMifratz<\/b><br>Incidence: 113 per 100,000<br>LISA p = 0.903<br>Cluster: Not significant","<b>Elkosh<\/b><br>Incidence: 374.5 per 100,000<br>LISA p = 0.519<br>Cluster: Not significant","<b>Fassuta<\/b><br>Incidence: 29.5 per 100,000<br>LISA p = 0.046<br>Cluster: Low–High outlier","<b>Gal'ed<\/b><br>Incidence: 169.5 per 100,000<br>LISA p = 0.289<br>Cluster: Not significant","<b>Hadera<\/b><br>Incidence: 0.9 per 100,000<br>LISA p = 0.248<br>Cluster: Not significant","<b>Haifa<\/b><br>Incidence: 5.7 per 100,000<br>LISA p = 0.411<br>Cluster: Not significant","<b>Iksal<\/b><br>Incidence: 6.7 per 100,000<br>LISA p = 0.37<br>Cluster: Not significant","<b>Jadeidi-Makr<\/b><br>Incidence: 5 per 100,000<br>LISA p = 0.477<br>Cluster: Not significant","<b>Kabri<\/b><br>Incidence: 91.8 per 100,000<br>LISA p = 0.88<br>Cluster: Not significant","<b>Kafr Manda<\/b><br>Incidence: 4.6 per 100,000<br>LISA p = 0.863<br>Cluster: Not significant","<b>Karmiel<\/b><br>Incidence: 4.1 per 100,000<br>LISA p = 0.409<br>Cluster: Not significant","<b>Katzrin<\/b><br>Incidence: 12.4 per 100,000<br>LISA p = 0.997<br>Cluster: Not significant","<b>Kiryat Ata<\/b><br>Incidence: 9.7 per 100,000<br>LISA p = 0.367<br>Cluster: Not significant","<b>Kiryat Bialik<\/b><br>Incidence: 6.5 per 100,000<br>LISA p = 0.387<br>Cluster: Not significant","<b>Kiryat Motzkin<\/b><br>Incidence: 3.9 per 100,000<br>LISA p = 0.939<br>Cluster: Not significant","<b>Kiryat Shmona<\/b><br>Incidence: 8.2 per 100,000<br>LISA p = 0.003<br>Cluster: Low–High outlier","<b>Kiryat Yam<\/b><br>Incidence: 4.7 per 100,000<br>LISA p = 0.392<br>Cluster: Not significant","<b>Kisra-Sumei<\/b><br>Incidence: 10.8 per 100,000<br>LISA p = 0.434<br>Cluster: Not significant","<b>Lohamei HaGeta'ot<\/b><br>Incidence: 102.2 per 100,000<br>LISA p = 0.995<br>Cluster: Not significant","<b>Migdal HaEmek<\/b><br>Incidence: 7 per 100,000<br>LISA p = 0.355<br>Cluster: Not significant","<b>Nahariya<\/b><br>Incidence: 4.4 per 100,000<br>LISA p = 0.634<br>Cluster: Not significant","<b>Nahf<\/b><br>Incidence: 7.2 per 100,000<br>LISA p = 0.448<br>Cluster: Not significant","<b>Nazareth<\/b><br>Incidence: 5.2 per 100,000<br>LISA p = 0.388<br>Cluster: Not significant","<b>Nesher<\/b><br>Incidence: 8.8 per 100,000<br>LISA p = 0.38<br>Cluster: Not significant","<b>Nof HaGalil<\/b><br>Incidence: 6.6 per 100,000<br>LISA p = 0.361<br>Cluster: Not significant","<b>Sajur<\/b><br>Incidence: 23.4 per 100,000<br>LISA p = 0.394<br>Cluster: Not significant","<b>Sakhnin<\/b><br>Incidence: 6 per 100,000<br>LISA p = 0.86<br>Cluster: Not significant","<b>Sha'ab<\/b><br>Incidence: 13.1 per 100,000<br>LISA p = 0.821<br>Cluster: Not significant","<b>Sha'ar HaAmakim<\/b><br>Incidence: 125.9 per 100,000<br>LISA p = 0.808<br>Cluster: Not significant","<b>Shamir<\/b><br>Incidence: 120 per 100,000<br>LISA p = 0.472<br>Cluster: Not significant","<b>Shefa-Amr<\/b><br>Incidence: 2.3 per 100,000<br>LISA p = 0.936<br>Cluster: Not significant","<b>Tamra<\/b><br>Incidence: 2.8 per 100,000<br>LISA p = 0.861<br>Cluster: Not significant","<b>Tiberias<\/b><br>Incidence: 3.9 per 100,000<br>LISA p = 0.827<br>Cluster: Not significant","<b>Tirat Carmel<\/b><br>Incidence: 6.3 per 100,000<br>LISA p = 0.384<br>Cluster: Not significant","<b>Tuba-Zangariyye<\/b><br>Incidence: 14.1 per 100,000<br>LISA p = 0.983<br>Cluster: Not significant","<b>Tur'an<\/b><br>Incidence: 6.8 per 100,000<br>LISA p = 0.379<br>Cluster: Not significant","<b>Yafa an-Naseriyye<\/b><br>Incidence: 5.4 per 100,000<br>LISA p = 0.362<br>Cluster: Not significant","<b>Yarka<\/b><br>Incidence: 6 per 100,000<br>LISA p = 0.394<br>Cluster: Not significant","<b>Yodfat<\/b><br>Incidence: 100 per 100,000<br>LISA p = 0.423<br>Cluster: Not significant"],null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addLegend","args":[{"colors":["#91BFDB","#CCCCCC"],"labels":["Low–High outlier","Not significant"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"topright","type":"factor","title":"LISA cluster type","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[32.45,33.2075],"lng":[34.916666666667,35.690555555556]}},"evals":[],"jsHooks":[]}</script>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-lisa-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;8: Local Moran’s I (LISA) analysis of LC incidence across Northern Israel. Localities are classified by cluster type: hotspots (High–High), coldspots (Low–Low), high–low outliers, and low–high outliers. Only localities with p &lt; 0.05 are highlighted; non-significant localities are shown in grey.
</figcaption>
</figure>
</div>
</div>
<div class="cell">
<div id="tbl-lisa" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-lisa-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;3: Localities classified as statistically significant LISA clusters (p &lt; 0.05). All three identified localities are Low–High outliers — low incidence surrounded by higher-incidence neighbors.
</figcaption>
<div aria-describedby="tbl-lisa-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output-display">
<table class="table table-striped table-hover do-not-create-environment cell caption-top table-sm small">
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Locality</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Population</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Cases (n)</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Incidence (per 100,000)</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Local Moran's I</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">p-value</th>
<th style="text-align: left;" data-quarto-table-cell-role="th">Cluster type</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Beitegen</td>
<td style="text-align: right;">12287</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">8.1</td>
<td style="text-align: right;">-0.433</td>
<td style="text-align: right;">0.049</td>
<td style="text-align: left;">Low–High outlier</td>
</tr>
<tr class="even">
<td style="text-align: left;">Fassuta</td>
<td style="text-align: right;">3387</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">29.5</td>
<td style="text-align: right;">-0.085</td>
<td style="text-align: right;">0.046</td>
<td style="text-align: left;">Low–High outlier</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Kiryat Shmona</td>
<td style="text-align: right;">24254</td>
<td style="text-align: right;">2</td>
<td style="text-align: right;">8.2</td>
<td style="text-align: right;">-0.596</td>
<td style="text-align: right;">0.003</td>
<td style="text-align: left;">Low–High outlier</td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
</section>
<section id="smoking-as-a-confounder" class="level4">
<h4 class="anchored" data-anchor-id="smoking-as-a-confounder">Smoking as a confounder</h4>
<p>To evaluate whether the observed spatial patterns could be explained by smoking prevalence, the population-adjusted distribution of smoker cases was mapped (<em><a href="#fig-smoking-kde" class="quarto-xref">Figure&nbsp;9</a></em>) and stratified Moran’s I analyses were performed. Among smokers only, Moran’s I was 0.026 (p = 0.725); among non-smokers only, Moran’s I was −0.032 (p = 0.626) — neither significant. The statistically significant clustering observed in the combined unadjusted dataset thus disappeared once smoking status was accounted for, identifying smoking as a true spatial confounder rather than an independent geographic risk factor.</p>
<div class="cell">
<div id="fig-smoking-kde" class="cell-output-display quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-smoking-kde-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="leaflet html-widget html-fill-item" id="htmlwidget-28853db76a309239b663" style="width:100%;height:464px;"></div>
<script type="application/json" data-for="htmlwidget-28853db76a309239b663">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addCircleMarkers","args":[[32.9575,32.926111111111,32.719722222222,32.496111111111,32.656197222222,33.049444444444,32.556397222222,32.45,32.8191666666667,32.6825,33.020897222222,32.9,32.8,33.2075,32.833055555556,32.678611111111,33.003611111111,32.702102777778,32.7711111111111,32.716666666667,32.866666666667,32.888888888889,32.7219444444444,32.805555555556,32.851147222222,32.789722222222,32.766666666667],[35.172777777778,35.083888888889,35.144166666667,35.498888888889,35.417097222222,35.305833333333,35.076997222222,34.916666666667,34.9991666666667,35.324444444444,35.149,35.283333333333,35.1,35.569722222222,35.066388888889,35.244444444444,35.0925,35.303333,35.0394444444444,35.333333333333,35.3,35.236944444444,35.1116666666667,35.169444444444,35.207108333333,35.524722222222,34.966666666667],[7.630307388663631,7.815589890253698,27.11166791717655,6.706510788632087,21.87497852352299,13.63500671198723,29.99999999784252,4,5.445095746607332,7.486299603725909,22.58639589538789,5.00437835353063,7.386085029702714,8.107403562717376,6.606272235048337,7.622790207642094,4.522262216589745,5.384398827719717,6.43430561538606,5.107399348321375,5.656296746610179,9.714929548809378,26.12432790693268,5.186619114953052,5.495321674399257,6.164639607149778,7.314478860797955],null,null,{"interactive":true,"className":"","stroke":false,"color":"#03F","weight":5,"opacity":0.5,"fill":true,"fillColor":"steelblue","fillOpacity":[0.2216723538562972,0.2233191055446989,0.6822963452866955,0.2142453736126819,0.5014322599980182,0.3017639419514396,0.7999999999964404,0.2,0.206212051707932,0.2204287075490386,0.5235411157167762,0.2039790226255731,0.2195819747423511,0.2260191538148126,0.2135179922821316,0.2216066510920897,0.2018764785806061,0.2058868726220775,0.2123059138611743,0.2044744046176465,0.2073874603579116,0.2432279654793196,0.6449872038852431,0.2048663777843893,0.2064853938051304,0.2104962932425814,0.2189863689908433]},null,null,["<b>Abu Snan<\/b><br>Smoker cases: 1<br>Adjusted intensity: 7 per 100,000","<b>Acre<\/b><br>Smoker cases: 4<br>Adjusted intensity: 7.5 per 100,000","<b>Alonim<\/b><br>Smoker cases: 1<br>Adjusted intensity: 136.4 per 100,000","<b>Beit She'an<\/b><br>Smoker cases: 1<br>Adjusted intensity: 4.9 per 100,000","<b>Ein Dor<\/b><br>Smoker cases: 1<br>Adjusted intensity: 85.6 per 100,000","<b>Fassuta<\/b><br>Smoker cases: 1<br>Adjusted intensity: 29.5 per 100,000","<b>Gal'ed<\/b><br>Smoker cases: 1<br>Adjusted intensity: 169.5 per 100,000","<b>Hadera<\/b><br>Smoker cases: 1<br>Adjusted intensity: 0.9 per 100,000","<b>Haifa<\/b><br>Smoker cases: 8<br>Adjusted intensity: 2.7 per 100,000","<b>Iksal<\/b><br>Smoker cases: 1<br>Adjusted intensity: 6.7 per 100,000","<b>Kabri<\/b><br>Smoker cases: 1<br>Adjusted intensity: 91.8 per 100,000","<b>Karmiel<\/b><br>Smoker cases: 1<br>Adjusted intensity: 2.1 per 100,000","<b>Kiryat Ata<\/b><br>Smoker cases: 4<br>Adjusted intensity: 6.4 per 100,000","<b>Kiryat Shmona<\/b><br>Smoker cases: 2<br>Adjusted intensity: 8.2 per 100,000","<b>Kiryat Yam<\/b><br>Smoker cases: 2<br>Adjusted intensity: 4.7 per 100,000","<b>Migdal HaEmek<\/b><br>Smoker cases: 2<br>Adjusted intensity: 7 per 100,000","<b>Nahariya<\/b><br>Smoker cases: 1<br>Adjusted intensity: 1.5 per 100,000","<b>Nazareth<\/b><br>Smoker cases: 2<br>Adjusted intensity: 2.6 per 100,000","<b>Nesher<\/b><br>Smoker cases: 1<br>Adjusted intensity: 4.4 per 100,000","<b>Nof HaGalil<\/b><br>Smoker cases: 1<br>Adjusted intensity: 2.2 per 100,000","<b>Sakhnin<\/b><br>Smoker cases: 1<br>Adjusted intensity: 3 per 100,000","<b>Sha'ab<\/b><br>Smoker cases: 1<br>Adjusted intensity: 13.1 per 100,000","<b>Sha'ar HaAmakim<\/b><br>Smoker cases: 1<br>Adjusted intensity: 125.9 per 100,000","<b>Shefa-Amr<\/b><br>Smoker cases: 1<br>Adjusted intensity: 2.3 per 100,000","<b>Tamra<\/b><br>Smoker cases: 1<br>Adjusted intensity: 2.8 per 100,000","<b>Tiberias<\/b><br>Smoker cases: 2<br>Adjusted intensity: 3.9 per 100,000","<b>Tirat Carmel<\/b><br>Smoker cases: 2<br>Adjusted intensity: 6.3 per 100,000"],null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addControl","args":["<b>Smoker cases<\/b><br>(population-adjusted)","topright",null,"info legend"]}],"limits":{"lat":[32.45,33.2075],"lng":[34.916666666667,35.569722222222]}},"evals":[],"jsHooks":[]}</script>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-smoking-kde-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;9: Population-adjusted distribution of smoker cases across Northern Israel. Circle size and opacity reflect the proportion of smokers per locality population (per 100,000). Compare with Figure 3.2.5b to assess geographic overlap between smoking prevalence and LC incidence.
</figcaption>
</figure>
</div>
</div>
</section>
<section id="ethnicity-as-a-potential-determinant" class="level4">
<h4 class="anchored" data-anchor-id="ethnicity-as-a-potential-determinant">Ethnicity as a potential determinant</h4>
<p>The cohort comprised four ethnic groups: Jewish (68.1%), Arab (25.5%), Druze (5.3%), and Bedouin (1.1%). Smoking prevalence among Jewish and Arab cases was similar at 53.1% and 50.0%, respectively; no smokers were recorded among Druze or Bedouin cases (<em><a href="#tbl-ethnicity-smoking" class="quarto-xref">Table&nbsp;4</a></em>).</p>
<div class="cell">
<div id="tbl-ethnicity-smoking" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-ethnicity-smoking-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;4: LC cases and smoking prevalence by ethnic group
</figcaption>
<div aria-describedby="tbl-ethnicity-smoking-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output-display">
<table class="table table-striped table-hover do-not-create-environment cell caption-top table-sm small">
<thead>
<tr class="header">
<th style="text-align: left;" data-quarto-table-cell-role="th">Ethnicity</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Cases</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">% of total cases</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">Smokers</th>
<th style="text-align: right;" data-quarto-table-cell-role="th">% of smokers among the cases (%)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Jewish</td>
<td style="text-align: right;">64</td>
<td style="text-align: right;">68.1</td>
<td style="text-align: right;">34</td>
<td style="text-align: right;">53.1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Arab</td>
<td style="text-align: right;">24</td>
<td style="text-align: right;">25.5</td>
<td style="text-align: right;">12</td>
<td style="text-align: right;">50.0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Druze</td>
<td style="text-align: right;">5</td>
<td style="text-align: right;">5.3</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">0.0</td>
</tr>
<tr class="even">
<td style="text-align: left;">Bedouin</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">1.1</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">0.0</td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
<p>Fisher’s exact test comparing smoking prevalence between Jewish and Arab cases yielded p = 0.8151, indicating no statistically significant difference. Druze and Bedouin cases were excluded from formal testing due to zero observed smokers in both groups — a finding that may reflect cultural norms, small subgroup size, or referral patterns rather than true population prevalence.</p>
<p>The population-adjusted distribution of smoker cases shows partial geographic overlap with LC incidence, particularly in the coastal corridor and central inland zone. However, the correspondence is imperfect — consistent with the Moran’s I stratified analysis, which showed no significant spatial autocorrelation among smokers or non-smokers when examined separately. Together, these findings suggest that smoking shapes the overall geographic distribution of LC cases but does not fully explain it at the local level.</p>
<p>Together, these results suggest that ethnicity does not independently explain the geographic distribution of LC cases in this cohort; its role is more likely indirect, mediated through residential settlement patterns and their overlap with smoking prevalence.</p>
</section>
</section>
</section>
<section id="discussion" class="level2">
<h2 class="anchored" data-anchor-id="discussion">Discussion</h2>
<p>This analysis of 94 LC cases from the Lady Davis Carmel Medical Center demonstrates both the potential and the inherent limitations of hospital-based spatial epidemiology.</p>
<p>The molecular and histopathological findings align closely with internationally reported patterns: ACA was the dominant subtype, EGFR and KRAS were the most frequent actionable alterations, and PD-L1 positivity was concentrated in ACAs — consistent with current global literature on non-small-cell LC molecular profiling.</p>
<p>The spatial analyses tell a methodologically instructive story. Crude case mapping initially suggested geographic clustering centered on the city of Haifa and coastal urban centers. However, successive analytical steps overturned this impression: population adjustment redistributed apparent risk toward smaller inland localities; global Moran’s I confirmed the absence of statistically significant spatial autocorrelation; and LISA identified only three Low–High outliers, with no true hotspots. Most critically, stratified Moran’s I analyses demonstrated that the modest clustering signal in the unadjusted data dissolved entirely once smoking status was accounted for — identifying smoking as the primary spatial confounder.</p>
<p>These findings underscore a fundamental principle of spatial epidemiology: apparent geographic clusters of disease cannot be interpreted at face value. Rigorous adjustment for population denominators and behavioral confounders is essential before attributing geographic gradients to environmental or other structural causes.</p>
<p>Several <strong>limitations</strong> constrain the interpretation of these findings:</p>
<ol type="1">
<li><p>The dataset is hospital-based and subject to referral bias — patients residing closer to Carmel Medical Center are overrepresented, which may inflate apparent case density in the Haifa metropolitan area.</p></li>
<li><p>The sample size of 94 cases limits statistical power for detecting subtle spatial patterns or subgroup differences.</p></li>
<li><p>Smoking history was recorded as a binary variable, precluding dose-response analysis.</p></li>
<li><p>Ethnic origin was approximated from patient names and residential locality rather than confirmed by self-report, introducing potential misclassification.</p></li>
<li><p>Individual-level environmental exposure data were not available, preventing a formal assessment of gene-environment interactions — the original motivation for this study design.</p></li>
</ol>
<p>Future investigations combining population-based cancer registry data, individual-level exposure measurements, and larger molecularly characterized cohorts would be needed to address these limitations and draw causal inferences.</p>
</section>
<section id="conclusions" class="level2">
<h2 class="anchored" data-anchor-id="conclusions">Conclusions</h2>
<p>This analysis demonstrates that apparent geographic clustering of LC cases in Northern Israel, when subjected to rigorous spatial analysis, is explained by the uneven distribution of smoking prevalence and population size across localities rather than by independent environmental or structural risk factors. Global Moran’s I showed no significant spatial autocorrelation (I = −0.008, p = 0.373), and LISA identified only three Low–High outliers — no true hotspots. Stratified analysis confirmed smoking as the primary spatial confounder.</p>
<p>At the molecular level, the cohort mirrors internationally reported patterns: ACA was the dominant histological subtype, EGFR and KRAS were the most frequently altered actionable drivers, and PD-L1 positivity was concentrated in ACAs. These findings support the continued integration of comprehensive molecular profiling into routine LC diagnostics.</p>
<p>The principal methodological contribution of this work is the demonstration that sequential adjustment — for population denominators, referral bias, and behavioral confounders — can overturn superficially compelling geographic patterns. This principle applies broadly in spatial epidemiology and is of particular relevance in hospital-based studies with limited catchment area coverage.</p>
<p>Future work should prioritize population-based cohort designs, individual-level environmental exposure measurements, and extended molecular datasets to enable formal assessment of gene-environment interactions in lung carcinogenesis.</p>
</section>

</main>
<!-- /main column -->
<script id="quarto-html-after-body" type="application/javascript">
  window.document.addEventListener("DOMContentLoaded", function (event) {
    const icon = "";
    const anchorJS = new window.AnchorJS();
    anchorJS.options = {
      placement: 'right',
      icon: icon
    };
    anchorJS.add('.anchored');
    const isCodeAnnotation = (el) => {
      for (const clz of el.classList) {
        if (clz.startsWith('code-annotation-')) {
          return true;
        }
      }
      return false;
    }
    const onCopySuccess = function(e) {
      // button target
      const button = e.trigger;
      // don't keep focus
      button.blur();
      // flash "checked"
      button.classList.add('code-copy-button-checked');
      var currentTitle = button.getAttribute("title");
      button.setAttribute("title", "Copied!");
      let tooltip;
      if (window.bootstrap) {
        button.setAttribute("data-bs-toggle", "tooltip");
        button.setAttribute("data-bs-placement", "left");
        button.setAttribute("data-bs-title", "Copied!");
        tooltip = new bootstrap.Tooltip(button,
          { trigger: "manual",
            customClass: "code-copy-button-tooltip",
            offset: [0, -8]});
        tooltip.show();
      }
      setTimeout(function() {
        if (tooltip) {
          tooltip.hide();
          button.removeAttribute("data-bs-title");
          button.removeAttribute("data-bs-toggle");
          button.removeAttribute("data-bs-placement");
        }
        button.setAttribute("title", currentTitle);
        button.classList.remove('code-copy-button-checked');
      }, 1000);
      // clear code selection
      e.clearSelection();
    }
    const getTextToCopy = function(trigger) {
      const outerScaffold = trigger.parentElement.cloneNode(true);
      const codeEl = outerScaffold.querySelector('code');
      for (const childEl of codeEl.children) {
        if (isCodeAnnotation(childEl)) {
          childEl.remove();
        }
      }
      return codeEl.innerText;
    }
    const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
      text: getTextToCopy
    });
    clipboard.on('success', onCopySuccess);
    if (window.document.getElementById('quarto-embedded-source-code-modal')) {
      const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
        text: getTextToCopy,
        container: window.document.getElementById('quarto-embedded-source-code-modal')
      });
      clipboardModal.on('success', onCopySuccess);
    }
      var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
      var mailtoRegex = new RegExp(/^mailto:/);
        var filterRegex = new RegExp('/' + window.location.host + '/');
      var isInternal = (href) => {
          return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
      }
      // Inspect non-navigation links and adorn them if external
     var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
      for (var i=0; i<links.length; i++) {
        const link = links[i];
        if (!isInternal(link.href)) {
          // undo the damage that might have been done by quarto-nav.js in the case of
          // links that we want to consider external
          if (link.dataset.originalHref !== undefined) {
            link.href = link.dataset.originalHref;
          }
        }
      }
    function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
      const config = {
        allowHTML: true,
        maxWidth: 500,
        delay: 100,
        arrow: false,
        appendTo: function(el) {
            return el.parentElement;
        },
        interactive: true,
        interactiveBorder: 10,
        theme: 'quarto',
        placement: 'bottom-start',
      };
      if (contentFn) {
        config.content = contentFn;
      }
      if (onTriggerFn) {
        config.onTrigger = onTriggerFn;
      }
      if (onUntriggerFn) {
        config.onUntrigger = onUntriggerFn;
      }
      window.tippy(el, config);
    }
    const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
    for (var i=0; i<noterefs.length; i++) {
      const ref = noterefs[i];
      tippyHover(ref, function() {
        // use id or data attribute instead here
        let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
        try { href = new URL(href).hash; } catch {}
        const id = href.replace(/^#\/?/, "");
        const note = window.document.getElementById(id);
        if (note) {
          return note.innerHTML;
        } else {
          return "";
        }
      });
    }
    const xrefs = window.document.querySelectorAll('a.quarto-xref');
    const processXRef = (id, note) => {
      // Strip column container classes
      const stripColumnClz = (el) => {
        el.classList.remove("page-full", "page-columns");
        if (el.children) {
          for (const child of el.children) {
            stripColumnClz(child);
          }
        }
      }
      stripColumnClz(note)
      if (id === null || id.startsWith('sec-')) {
        // Special case sections, only their first couple elements
        const container = document.createElement("div");
        if (note.children && note.children.length > 2) {
          container.appendChild(note.children[0].cloneNode(true));
          for (let i = 1; i < note.children.length; i++) {
            const child = note.children[i];
            if (child.tagName === "P" && child.innerText === "") {
              continue;
            } else {
              container.appendChild(child.cloneNode(true));
              break;
            }
          }
          if (window.Quarto?.typesetMath) {
            window.Quarto.typesetMath(container);
          }
          return container.innerHTML
        } else {
          if (window.Quarto?.typesetMath) {
            window.Quarto.typesetMath(note);
          }
          return note.innerHTML;
        }
      } else {
        // Remove any anchor links if they are present
        const anchorLink = note.querySelector('a.anchorjs-link');
        if (anchorLink) {
          anchorLink.remove();
        }
        if (window.Quarto?.typesetMath) {
          window.Quarto.typesetMath(note);
        }
        if (note.classList.contains("callout")) {
          return note.outerHTML;
        } else {
          return note.innerHTML;
        }
      }
    }
    for (var i=0; i<xrefs.length; i++) {
      const xref = xrefs[i];
      tippyHover(xref, undefined, function(instance) {
        instance.disable();
        let url = xref.getAttribute('href');
        let hash = undefined;
        if (url.startsWith('#')) {
          hash = url;
        } else {
          try { hash = new URL(url).hash; } catch {}
        }
        if (hash) {
          const id = hash.replace(/^#\/?/, "");
          const note = window.document.getElementById(id);
          if (note !== null) {
            try {
              const html = processXRef(id, note.cloneNode(true));
              instance.setContent(html);
            } finally {
              instance.enable();
              instance.show();
            }
          } else {
            // See if we can fetch this
            fetch(url.split('#')[0])
            .then(res => res.text())
            .then(html => {
              const parser = new DOMParser();
              const htmlDoc = parser.parseFromString(html, "text/html");
              const note = htmlDoc.getElementById(id);
              if (note !== null) {
                const html = processXRef(id, note);
                instance.setContent(html);
              }
            }).finally(() => {
              instance.enable();
              instance.show();
            });
          }
        } else {
          // See if we can fetch a full url (with no hash to target)
          // This is a special case and we should probably do some content thinning / targeting
          fetch(url)
          .then(res => res.text())
          .then(html => {
            const parser = new DOMParser();
            const htmlDoc = parser.parseFromString(html, "text/html");
            const note = htmlDoc.querySelector('main.content');
            if (note !== null) {
              // This should only happen for chapter cross references
              // (since there is no id in the URL)
              // remove the first header
              if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
                note.children[0].remove();
              }
              const html = processXRef(null, note);
              instance.setContent(html);
            }
          }).finally(() => {
            instance.enable();
            instance.show();
          });
        }
      }, function(instance) {
      });
    }
        let selectedAnnoteEl;
        const selectorForAnnotation = ( cell, annotation) => {
          let cellAttr = 'data-code-cell="' + cell + '"';
          let lineAttr = 'data-code-annotation="' +  annotation + '"';
          const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
          return selector;
        }
        const selectCodeLines = (annoteEl) => {
          const doc = window.document;
          const targetCell = annoteEl.getAttribute("data-target-cell");
          const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
          const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
          const lines = annoteSpan.getAttribute("data-code-lines").split(",");
          const lineIds = lines.map((line) => {
            return targetCell + "-" + line;
          })
          let top = null;
          let height = null;
          let parent = null;
          if (lineIds.length > 0) {
              //compute the position of the single el (top and bottom and make a div)
              const el = window.document.getElementById(lineIds[0]);
              top = el.offsetTop;