G - MPX NYC: Results

G.1 People

Race and gender

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Code

make_table_freq1(demo_group) |>  
  plot_bar() +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc() +
  scale_fill_mpxnyc(name = "Race / gender")

Code

make_table_freq1(demo_group) |>
  draw_table_1("Race-Gender")

Race-Gender
(MPX NYC, 2022)
	N	% (CI)
Another demographic	26	2% (1-3)
Black cisgender man	131	10% (8-12)
Cisgender woman	62	5% (4-6)
Latinx cisgender man	198	15% (13-17)
Non binary	96	7% (6-9)
Other cisgender man	176	13% (12-15)
Transgender man	34	3% (2-3)
Transgender woman	29	2% (1-3)
White cisgender man	552	42% (40-45)

Code

  make_table_freq1(age) |>
    plot_bar() +
    theme_mpxnyc_bar() +
    scale_axis_mpxnyc() +
    scale_fill_mpxnyc(name = "Age group")

Code

make_table_freq1(age) |>
  draw_table_1("Age")

Age
(MPX NYC, 2022)
	N	% (CI)
18-24	195	15% (13-17)
25-34	538	41% (39-44)
35-44	325	25% (23-27)
45-54	145	11% (9-13)
55+	101	8% (6-9)

Recruitment Channel

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Code

make_table_freq1(channel) |>
  plot_bar()  +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc() +
  scale_fill_mpxnyc(name = "Recruitment\nchannel")

Code

make_table_freq1(channel) |>
  draw_table_1("Channel")

Channel
(MPX NYC, 2022)
	N	% (CI)
Grindr	706	54% (51-57)
Instagram	112	9% (7-10)
Partner toolkit	142	11% (9-13)
Twitter	62	5% (4-6)
Unknown	282	22% (19-24)

Gender identity

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Code

make_table_freq1(genderId) |>  
  plot_bar() +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc("MPX NYC sample proportion") +
  scale_fill_mpxnyc(name = "Gender\nmodality")

Code

make_table_freq1(genderId) |>
  draw_table_1("Gender")

Gender
(MPX NYC, 2022)
	N	% (CI)
Another Demographic	26	2% (1-3)
Cisgender Man	1057	81% (79-83)
Cisgender Woman	62	5% (4-6)
Non Binary	96	7% (6-9)
Transgender Man	34	3% (2-3)
Transgender Woman	29	2% (1-3)

Sexual Orientation

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Code

make_table_freq1(sexOrientation) |>    
  plot_bar() +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc() +
  scale_fill_mpxnyc(name = "Sexual\norientation")

Code

make_table_freq1(sexOrientation) |>
  draw_table_1("Sexual Orientation")

Sexual Orientation
(MPX NYC, 2022)
	N	% (CI)
Bisexual	186	14% (12-16)
Gay	860	66% (63-68)
Queer	159	12% (10-14)
Something Else	35	3% (2-4)
Straight	64	5% (4-6)

Sexual or physical contact with more than two other people at the same time

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Code

make_table_freq1(groupSex) |>  
  plot_bar()  +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc() +
  scale_fill_mpxnyc(name = "Group contact")

Code

make_table_freq1(groupSex) |> 
  draw_table_1("Group Sex / Contact past 4 weeks")

Group Sex / Contact past 4 weeks
(MPX NYC, 2022)
	N	% (CI)
No	774	59% (57-62)
Yes	530	41% (38-43)

Race

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Code

make_table_freq1(race) |>
  plot_bar()  +
  theme_mpxnyc_bar() +
  scale_axis_mpxnyc() +
  scale_fill_mpxnyc(name = "Race")

Code

make_table_freq1(race) |>
  draw_table_1("Race")

Race
(MPX NYC, 2022)
	N	% (CI)
Another Group	57	4% (3-5)
Asian	84	6% (5-8)
Black	156	12% (10-14)
Latinx	231	18% (16-20)
Multi Racial	79	6% (5-7)
White	695	53% (51-56)

Sexual orientation by race and gender

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Code

make_table_freq2(sexOrientation, demo_group) |>
  plot_radar_grid()   +
  scale_radial_mpxnyc() +
  scale_fill_mpxnyc(name = "") +
  theme_mpxnyc_radar_people()

Code

make_table_freq2(sexOrientation, demo_group)  |>
  draw_table_2("Sexual Orientation by Race-Gender")

Sexual Orientation by Race-Gender
(MPX NYC, 2022)
	N	% (CI)
Another demographic
Bisexual	3	12% (3-26)
Gay	5	19% (5-35)
Queer	11	42% (24-62)
Something Else	6	23% (7-39)
Straight	1	4% (3-15)
Black cisgender man
Bisexual	24	18% (12-25)
Gay	88	67% (59-76)
Queer	9	7% (3-11)
Something Else	4	3% (1-6)
Straight	6	5% (1-9)
Cisgender woman
Bisexual	19	31% (20-42)
Gay	4	6% (2-13)
Queer	16	26% (15-37)
Straight	23	37% (25-49)
Latinx cisgender man
Bisexual	24	12% (8-17)
Gay	158	80% (74-86)
Queer	9	5% (2-8)
Something Else	2	1% (0-3)
Straight	5	3% (1-5)
Non binary
Bisexual	18	19% (12-26)
Gay	24	25% (17-34)
Queer	53	55% (45-65)
Something Else	1	1% (1-4)
Other cisgender man
Bisexual	24	14% (9-19)
Gay	136	77% (71-83)
Queer	10	6% (2-9)
Straight	6	3% (1-6)
Transgender man
Bisexual	10	29% (13-46)
Gay	7	21% (9-35)
Queer	14	41% (24-59)
Something Else	1	3% (2-11)
Straight	2	6% (2-15)
Transgender woman
Bisexual	7	24% (11-39)
Gay	3	10% (3-24)
Queer	4	14% (4-29)
Something Else	6	21% (7-37)
Straight	9	31% (15-50)
White cisgender man
Bisexual	57	10% (8-13)
Gay	435	79% (75-82)
Queer	33	6% (4-8)
Something Else	15	3% (1-4)
Straight	12	2% (1-3)

HIV and PrEP status by race and gender

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Code

make_table_freq2(hivPrep, demo_group) |>
  plot_radar_grid()  +
  scale_radial_mpxnyc() +
  scale_fill_mpxnyc(name = "") +
  theme_mpxnyc_radar_people()

Code

make_table_freq2(hivPrep, demo_group) |>
  draw_table_2("HIV and PrEP Status by Race-Gender")

HIV and PrEP Status by Race-Gender
(MPX NYC, 2022)
	N	% (CI)
Another demographic
No	11	48% (23-61)
Yes	12	52% (26-65)
Black cisgender man
No	56	62% (34-51)
Yes	34	38% (19-33)
Cisgender woman
No	61	100% (94-100)
Latinx cisgender man
No	95	56% (41-55)
Yes	74	44% (31-44)
Non binary
No	62	68% (55-73)
Yes	29	32% (22-40)
Other cisgender man
No	92	56% (45-60)
Yes	73	44% (34-49)
Transgender man
No	23	68% (50-82)
Yes	11	32% (18-50)
Transgender woman
No	16	64% (37-72)
Yes	9	36% (14-49)
White cisgender man
No	242	48% (39-48)
Yes	263	52% (44-52)

MPOX vaccination status by race and gender

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Code

make_table_freq2(monkeypoxVaccine, demo_group) |>
  plot_radar_grid()  +
  scale_radial_mpxnyc() +
  scale_fill_mpxnyc(name = "") +
  theme_mpxnyc_radar_people()

Code

make_table_freq2(monkeypoxVaccine, demo_group) |>
  draw_table_2( "Mpox Vax Status by Race-Gender")

Mpox Vax Status by Race-Gender
(MPX NYC, 2022)
	N	% (CI)
Another demographic
No	8	31% (14-48)
Unsure	1	4% (3-15)
Yes	17	65% (48-83)
Black cisgender man
No	50	38% (29-46)
Yes	81	62% (54-71)
Cisgender woman
No	51	82% (73-91)
Unsure	1	2% (1-6)
Yes	10	16% (7-25)
Latinx cisgender man
No	77	39% (32-46)
Yes	121	61% (54-68)
Non binary
No	32	33% (24-43)
Unsure	1	1% (1-4)
Yes	63	66% (56-75)
Other cisgender man
No	51	29% (22-36)
Unsure	2	1% (1-3)
Yes	123	70% (63-77)
Transgender man
No	10	29% (15-46)
Yes	24	71% (54-85)
Transgender woman
No	17	59% (41-77)
Unsure	1	3% (3-12)
Yes	11	38% (20-56)
White cisgender man
No	131	24% (20-27)
Unsure	1	0% (0-1)
Yes	420	76% (73-80)

G.2 Places

Type of venue for contact vs. type of contact

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Code

make_table_freq2(placeType, placeSex, person_analysis = FALSE) |>
  plot_radar_stratified() +
  scale_radial_mpxnyc() +
  scale_fill_mpxnyc(name = "") +
  theme_mpxnyc_radar_places()

Code

make_table_freq2(placeType, placeSex, person_analysis = FALSE) |>
  draw_table_2("Venue type vs. sexual contact")

Venue type vs. sexual contact
(MPX NYC, 2022)
	N	% (CI)
Did not have sex
Concert/Theatre/Show	63	24% (18-29)
Dance Party	110	42% (35-48)
Dark Room/Sex Party	10	4% (2-6)
Private Residence	28	11% (7-14)
Something Else	40	15% (11-20)
Sport Game	13	5% (2-8)
Had sex
Concert/Theatre/Show	4	1% (0-2)
Dance Party	33	9% (6-13)
Dark Room/Sex Party	52	15% (11-19)
Private Residence	222	63% (57-68)
Something Else	40	11% (8-15)
Sport Game	1	0% (0-1)

Type of venue for contact vs. distance from home

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Code

make_table_freq2(placeType, distanceFromHome, person_analysis = FALSE) |>
  plot_radar_stratified() +
  scale_fill_mpxnyc(name = "") +
  scale_radial_mpxnyc() +
  theme_mpxnyc_radar_places()

Code

make_table_freq2(placeType, distanceFromHome, person_analysis = FALSE) |>
  draw_table_2("Venue type vs. distance from home")

Venue type vs. distance from home
(MPX NYC, 2022)
	N	% (CI)
Different Borough
Concert/Theatre/Show	26	13% (8-18)
Dance Party	64	32% (25-38)
Dark Room/Sex Party	29	14% (10-20)
Private Residence	47	23% (17-31)
Something Else	25	12% (7-18)
Sport Game	9	4% (2-7)
Same Borough
Concert/Theatre/Show	36	16% (11-21)
Dance Party	56	24% (19-30)
Dark Room/Sex Party	22	10% (6-13)
Private Residence	73	32% (25-38)
Something Else	39	17% (12-22)
Sport Game	4	2% (0-4)
Same Community District
Concert/Theatre/Show	5	3% (1-5)
Dance Party	23	12% (8-18)
Dark Room/Sex Party	11	6% (3-10)
Private Residence	130	70% (63-77)
Something Else	16	9% (5-13)
Sport Game	1	1% (0-2)

Place type vs. Distance from home vs. Sex contact

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Code

make_table_freq3( placeType, distanceFromHome, placeSex, person_analysis = FALSE) |>
  plot_radar_stratified2()  +
  scale_fill_mpxnyc(name = "") +
  scale_radial_mpxnyc() +
  theme_mpxnyc_radar_places()

Code

make_table_freq3( placeType, distanceFromHome, placeSex, person_analysis = FALSE)  |>
  draw_table_3("Venue type by home distance and sexual contact")

Venue type by home distance and sexual contact
(MPX NYC, 2022)
	N	% (CI)
Different Borough - Did not have sex
Concert/Theatre/Show	23	22% (14-30)
Dance Party	50	47% (37-56)
Dark Room/Sex Party	4	4% (1-8)
Private Residence	8	8% (3-14)
Something Else	13	12% (6-19)
Sport Game	8	8% (3-13)
Different Borough - Had sex
Concert/Theatre/Show	3	3% (1-7)
Dance Party	14	15% (8-22)
Dark Room/Sex Party	25	27% (18-36)
Private Residence	39	41% (30-53)
Something Else	12	13% (6-21)
Sport Game	1	1% (1-4)
Same Borough - Did not have sex
Concert/Theatre/Show	35	30% (21-40)
Dance Party	43	37% (29-47)
Dark Room/Sex Party	3	3% (1-6)
Private Residence	10	9% (4-14)
Something Else	21	18% (11-26)
Sport Game	4	3% (1-7)
Same Borough - Had sex
Concert/Theatre/Show	1	1% (1-3)
Dance Party	13	11% (6-17)
Dark Room/Sex Party	19	17% (10-24)
Private Residence	63	55% (46-65)
Something Else	18	16% (10-22)
Same Community District - Did not have sex
Concert/Theatre/Show	5	12% (3-23)
Dance Party	17	40% (25-56)
Dark Room/Sex Party	3	7% (2-16)
Private Residence	10	24% (11-39)
Something Else	6	14% (5-25)
Sport Game	1	2% (2-9)
Same Community District - Had sex
Dance Party	6	4% (1-8)
Dark Room/Sex Party	8	6% (2-10)
Private Residence	120	83% (77-89)
Something Else	10	7% (3-11)

G.3 Spatial mixing

Spatial mixing coefficient by age

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Code

make_plotdata_age_mixing_matrix() |>
  plot_matrix_mixing() +
  scale_fill_mpxnyc_gradient() +
  scale_color_mpxnyc_gradient() +
  theme_mpxnyc_mixing(plot.margin = ggplot2::unit(10*c(10,10,10,10), "pt"))

Figure G.1: Social mixing matrix for MPX NYC participants by age

Code

make_table_mixing_2(age) |>
 draw_table_matrix_mixing("Age")

Age
(MPX NYC, 2022)
Ego group	Alter group	Coef (CI)
18-24	18-24	36% (17 to 57)
18-24	25-34	-3% (-10 to 4)
18-24	35-44	-4% (-13 to 5)
18-24	45-54	-11% (-23 to 1)
18-24	55+	-26% (-38 to -12)
25-34	18-24	-1% (-10 to 9)
25-34	25-34	11% (6 to 15)
25-34	35-44	-3% (-8 to 3)
25-34	45-54	-13% (-20 to -5)
25-34	55+	-28% (-37 to -19)
35-44	18-24	-2% (-12 to 8)
35-44	25-34	0% (-5 to 4)
35-44	35-44	13% (5 to 21)
35-44	45-54	-11% (-20 to -2)
35-44	55+	-18% (-30 to -6)
45-54	18-24	1% (-13 to 15)
45-54	25-34	-3% (-10 to 4)
45-54	35-44	-3% (-12 to 6)
45-54	45-54	23% (6 to 47)
45-54	55+	-10% (-27 to 8)
55+	18-24	-3% (-19 to 14)
55+	25-34	-9% (-18 to 0)
55+	35-44	-4% (-16 to 8)
55+	45-54	0% (-18 to 19)
55+	55+	65% (28 to 118)

Spatial mixing coefficient by race-Gender

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Code

make_plotdata_racegender_mixing_matrix() |>
  plot_matrix_mixing() +
  scale_fill_mpxnyc_gradient() +
  scale_color_mpxnyc_gradient() +
  theme_mpxnyc_mixing()

Figure G.2: Social mixing matrices for MPX NYC participants by race-gender

Code

make_table_mixing_2(demo_group) |>
 draw_table_matrix_mixing("Race-gender")

Race-gender
(MPX NYC, 2022)
Ego group	Alter group	Coef (CI)
White cisgender man	White cisgender man	12% (7 to 17)
White cisgender man	Latinx cisgender man	-11% (-19 to -1)
White cisgender man	Black cisgender man	-22% (-33 to -11)
White cisgender man	Transgender man	4% (-19 to 28)
White cisgender man	Transgender woman	-15% (-37 to 8)
White cisgender man	Non binary	-5% (-19 to 11)
White cisgender man	Cisgender woman	-10% (-23 to 3)
White cisgender man	Other cisgender man	-5% (-15 to 4)
White cisgender man	Another demographic	26% (-16 to 81)
Latinx cisgender man	White cisgender man	-16% (-23 to -9)
Latinx cisgender man	Latinx cisgender man	45% (25 to 69)
Latinx cisgender man	Black cisgender man	8% (-11 to 28)
Latinx cisgender man	Transgender man	20% (-19 to 57)
Latinx cisgender man	Transgender woman	-20% (-44 to 8)
Latinx cisgender man	Non binary	-6% (-23 to 14)
Latinx cisgender man	Cisgender woman	-18% (-38 to 4)
Latinx cisgender man	Other cisgender man	-2% (-16 to 12)
Latinx cisgender man	Another demographic	30% (-21 to 93)
Black cisgender man	White cisgender man	-24% (-33 to -16)
Black cisgender man	Latinx cisgender man	10% (-7 to 30)
Black cisgender man	Black cisgender man	99% (57 to 149)
Black cisgender man	Transgender man	-27% (-55 to 5)
Black cisgender man	Transgender woman	0% (-37 to 48)
Black cisgender man	Non binary	-11% (-33 to 12)
Black cisgender man	Cisgender woman	5% (-26 to 39)
Black cisgender man	Other cisgender man	-5% (-20 to 13)
Black cisgender man	Another demographic	38% (-26 to 119)
Transgender man	White cisgender man	-11% (-28 to 5)
Transgender man	Latinx cisgender man	7% (-24 to 43)
Transgender man	Black cisgender man	-17% (-54 to 25)
Transgender man	Transgender man	185% (20 to 477)
Transgender man	Transgender woman	-4% (-59 to 76)
Transgender man	Non binary	56% (-1 to 124)
Transgender man	Cisgender woman	-34% (-65 to 8)
Transgender man	Other cisgender man	-15% (-40 to 13)
Transgender man	Another demographic	12% (-50 to 98)
Transgender woman	White cisgender man	-10% (-27 to 5)
Transgender woman	Latinx cisgender man	-8% (-33 to 20)
Transgender woman	Black cisgender man	13% (-31 to 67)
Transgender woman	Transgender man	-4% (-59 to 71)
Transgender woman	Transgender woman	117% (2 to 322)
Transgender woman	Non binary	18% (-28 to 68)
Transgender woman	Cisgender woman	-6% (-51 to 56)
Transgender woman	Other cisgender man	-5% (-31 to 25)
Transgender woman	Another demographic	62% (-32 to 205)
Non binary	White cisgender man	-9% (-18 to 0)
Non binary	Latinx cisgender man	-11% (-27 to 5)
Non binary	Black cisgender man	-19% (-38 to 1)
Non binary	Transgender man	50% (-1 to 108)
Non binary	Transgender woman	6% (-35 to 53)
Non binary	Non binary	77% (39 to 125)
Non binary	Cisgender woman	-10% (-37 to 24)
Non binary	Other cisgender man	3% (-16 to 23)
Non binary	Another demographic	27% (-25 to 92)
Cisgender woman	White cisgender man	-3% (-15 to 8)
Cisgender woman	Latinx cisgender man	-5% (-26 to 19)
Cisgender woman	Black cisgender man	1% (-28 to 31)
Cisgender woman	Transgender man	-15% (-54 to 40)
Cisgender woman	Transgender woman	-18% (-56 to 32)
Cisgender woman	Non binary	-3% (-29 to 28)
Cisgender woman	Cisgender woman	89% (34 to 164)
Cisgender woman	Other cisgender man	-16% (-33 to 4)
Cisgender woman	Another demographic	48% (-35 to 150)
Other cisgender man	White cisgender man	-6% (-13 to 0)
Other cisgender man	Latinx cisgender man	-4% (-17 to 8)
Other cisgender man	Black cisgender man	-3% (-19 to 14)
Other cisgender man	Transgender man	-2% (-34 to 34)
Other cisgender man	Transgender woman	-19% (-40 to 5)
Other cisgender man	Non binary	2% (-16 to 23)
Other cisgender man	Cisgender woman	-16% (-35 to 5)
Other cisgender man	Other cisgender man	30% (13 to 52)
Other cisgender man	Another demographic	27% (-12 to 73)
Another demographic	White cisgender man	-3% (-21 to 12)
Another demographic	Latinx cisgender man	-6% (-32 to 24)
Another demographic	Black cisgender man	2% (-37 to 55)
Another demographic	Transgender man	-1% (-60 to 88)
Another demographic	Transgender woman	-21% (-62 to 31)
Another demographic	Non binary	-9% (-39 to 27)
Another demographic	Cisgender woman	-13% (-56 to 55)
Another demographic	Other cisgender man	2% (-24 to 34)
Another demographic	Another demographic	174% (-8 to 648)

Spatial mixing coefficient by sexual orientation

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Code

make_plotdata_sexorientation_mixing_matrix() |>
  plot_matrix_mixing() +
  scale_fill_mpxnyc_gradient() +
  scale_color_mpxnyc_gradient() +
  theme_mpxnyc_mixing(plot.margin = ggplot2::unit(10*c(10,10,10,10), "pt"))

Figure G.3: Social mixing matrix for MPX NYC participants by sexual orientation

Code

make_table_mixing_2(sexOrientation) |>
 draw_table_matrix_mixing("Sexual orientation")

Sexual orientation
(MPX NYC, 2022)
Ego group	Alter group	Coef (CI)
Gay	Gay	3% (0 to 5)
Gay	Bisexual	-7% (-14 to 0)
Gay	Straight	-16% (-31 to 2)
Gay	Queer	3% (-5 to 12)
Gay	Something Else	-15% (-31 to 0)
Bisexual	Gay	-5% (-9 to -1)
Bisexual	Bisexual	25% (9 to 44)
Bisexual	Straight	0% (-29 to 34)
Bisexual	Queer	0% (-13 to 14)
Bisexual	Something Else	-14% (-41 to 15)
Straight	Gay	-6% (-14 to 1)
Straight	Bisexual	2% (-20 to 26)
Straight	Straight	103% (19 to 223)
Straight	Queer	-9% (-33 to 16)
Straight	Something Else	-4% (-56 to 75)
Queer	Gay	-3% (-7 to 1)
Queer	Bisexual	-8% (-21 to 4)
Queer	Straight	-22% (-44 to 3)
Queer	Queer	42% (25 to 61)
Queer	Something Else	-30% (-55 to -1)
Something Else	Gay	-8% (-20 to 4)
Something Else	Bisexual	2% (-30 to 35)
Something Else	Straight	-5% (-52 to 62)
Something Else	Queer	-11% (-43 to 26)
Something Else	Something Else	245% (16 to 625)