Multiple conditions to determine status

Question

I am working with diagnostic criteria, therefore several conditions must be met, it is essential for my purpose to get prevalence. What I am trying to detect is:

• Number of individuals at the BEGINNING of the study meeting certain conditions (3/5)
• Number of individuals at the END of the study meeting certain conditions (3/5)
• How many individuals have improved their status
• How many individuals have worsened their status

My database

data <-
structure(list(paciente = structure(c(6186, 6287, 6002, 6007, 
6345, 6539, 6112, 6526, 6227, 6271, 6215, 6350, 6065, 6096, 6062, 
6119, 6290, 6293, 6372, 6036, 6434, 6463, 6506, 6004, 6147, 6026, 
6188, 6252, 6081, 6223, 6191, 6553, 6492, 6410, 6447, 6009, 6221, 
6260, 6452, 6066, 6368, 6181, 6051, 6208, 6502, 6158, 6059, 6379, 
6233, 6099), label = "Paciente", format.spss = "F6.0"), sexo_s1 = structure(c(2L, 
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L), .Label = c("Hombre", "Mujer"), label = "Sexo", class = "factor"), 
    edad_s1 = structure(c(66, 63, 70, 62, 64, 72, 66, 66, 69, 
    60, 70, 63, 60, 68, 66, 65, 66, 73, 63, 61, 66, 55, 69, 73, 
    65, 73, 58, 62, 72, 71, 67, 67, 61, 75, 67, 72, 70, 58, 62, 
    61, 71, 71, 63, 74, 60, 69, 72, 75, 67, 63), label = "Edad", format.spss = "F3.0"), 
    grupo_int_v00 = structure(c(1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 
    1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 
    2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L), .Label = c("A", 
    "B"), label = "Grupo de intervención", class = "factor"), 
    glucosa_v00 = structure(c(87, 101, 95, 136, 104, 108, 97, 
    107, 89, 85, 104, 99, 100, 127, 118, 109, 109, 108, 110, 
    100, 112, 103, 98, 100, 99, 182, 93, 148, 116, 128, 116, 
    133, 101, 112, 182, 165, 94, 142, 108, 112, 156, 181, 120, 
    126, 150, 93, 102, 161, 124, 152), label = "Analítica: Glucosa en mg/dL", format.spss = "F4.0"), 
    hdl_v00 = structure(c(72, 43, 90, 45, 50, 44, 81, 56, 68, 
    38, 47, 42, 69, 65, 52, 60, 50, 40, 47, 71, 52, 53, 69, 56, 
    55, 45, 51, 47, 68, 62, 71, 55, 64, 48, 35, 72, 45, 36, 52, 
    44, 72, 54, 59, 63, 57, 53, 59, 57, 50, 43), label = "Analítica: Colesterol HDL en mg/dL", format.spss = "F4.0"), 
    trigli_v00 = structure(c(284, 282, 126, 58, 153, 105, 138, 
    243, 102, 204, 189, 70, 160, 124, 191, 164, 205, 149, 152, 
    90, 151, 109, 135, 80, 136, 95, 217, 117, 116, 207, 217, 
    123, 100, 110, 648, 106, 204, 197, 95, 166, 88, 429, 101, 
    90, 87, 155, 143, 107, 98, 93), label = "Analítica: Triglicéridos en mg/dL", format.spss = "F5.0"), 
    tasis2_e_v00 = structure(c(146, 147, 130, 151, 145, 144, 
    138, 145, 138, 139, 146, 144, 117, 145, 126, 115, 148, 167, 
    131, 125, 123, 140, 146, 120, 123, 152, 146, 130, 100, 147, 
    139, 131, 145, 137, 151, 176, 138, 138, 122, 146, 139, 156, 
    137, 146, 149, 122, 145, 123, 161, 123), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"), 
    tadias2_e_v00 = structure(c(60, 82, 64, 88, 84, 78, 82, 84, 
    72, 85, 65, 76, 61, 71, 67, 72, 58, 85, 87, 81, 68, 82, 60, 
    53, 57, 71, 80, 64, 50, 82, 74, 84, 85, 71, 71, 110, 78, 
    77, 68, 88, 78, 89, 69, 84, 65, 68, 75, 54, 71, 65), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0"), 
    cintura1_v00 = structure(c(101.5, 97, 105.5, 126, 106.5, 
    108.5, 115, 94, 106, 126.5, 119, 106, 116.2, 94, 130.7, 101.5, 
    116.5, 112, 100, 111.6, 116.5, 115, 110.2, 109, 126, 94, 
    116.5, 112, 98, 126.5, 120, 119.5, 118.5, 112, 124, 102.5, 
    106, 135, 113.5, 113, 97.5, 123, 136.2, 103.5, 108, 109, 
    103, 103, 102.5, 123.2), label = "Cintura: 1a determinación", format.spss = "F5.1")), row.names = c(NA, 
-50L), class = "data.frame")

ID variable: paciente Sex: sexo_s1 Age: edad_s1 Treatment group: grupo_int_v00 (doesn't change)

Notation: suffixes

• _v00: baseline measurement
• _v01: endpoint measurement

The conditions to be positive are meeting at least 3 conditions (whichever they are) of the following:

1. glucosa_v00 > 100
2. trigli_v00 > 150
3. cintura1_v00 > 94 ( (if sexo_s1 = 0) or cintura1_v00 > 80 (if sexo_s1 = 1)
4. tasis2_e_v00 ≥ 140 or tadias2_e_v00 ≥ 85
5. hdl_v00 ≤ 50 (if sexo_s1 = 0) or hdl_v00 ≤ 40 (if sexo_s1 = 1)

I want to count (if possible per group: grupo_int_v00 = A/B) how many individuals meet at least 3 out of 5 at baseline (_v01) and at the end (_v01):

1. glucosa_v01 > 100
2. trigli_v01 > 150
3. cintura1_v01
4. tasis2_e_v01 ≥ 140 or tadias2_e_v01 ≥ 85
5. hdl_v01 ≤ 50 (if sexo_s1 = 0) or hdl_v01 ≤ 40 (if sexo_s1 = 1)

Objective

• My problem with conditional of this type is I don't know how to write correct syntax to express that 3 conditions, whichever the conditions are, is required to meet positive status, and how to make the conditions fit with sex values (sexo_s1)
• How to get the individuals that have worsened or improved their conditions e.g. patient 6357 has improved 2 variables, so he doesn't meet 3 conditions out of established 5. So, how many patients are like patient 6357. On the other how many patients before did not meet criteria, and now do

As I said I have tried several syntaxes, really basic conditionals but without further success Thanks in advance

Appreciate it