I used this note https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect074.htm to explore the use of lsmeans.
My new code is:
proc logistic  data=data1
order=data;
class Bact Sick/param=glm desc;
model  Posit/Trials =	Bact Sick Bact*Sick
/expb scale=none link=logit lackfit firth clodds=pl;
ods output cloddspl=firth;
   oddsratio Bact;
   lsmeans Bact / e diff oddsratio cl adjust=bon;
id idor;
run;
Class Level Information
Class	Value	Design Variables					
Sick	Z	1	0	0	0	0	
	W	0	1	0	0	0	
	Y	0	0	1	0	0	
	X	0	0	0	1	0	
	V	0	0	0	0	1	
Bact	E	1	0	0	0	0	
	B	0	1	0	0	0	
	D	0	0	1	0	0	
	C	0	0	0	1	0	
	A	0	0	0	0	1	

The table below shows that all Bact have significantly nonzero (infection "success rates") at the 0.05 level. 
Am I correct on my interpretation of this table?
Bact Least Squares Means								
Bact	Estimate	Standard Error	z Value	Pr > |z|	Alpha	Lower	Upper	
E	-3.7012	0.4459	-8.3	<.0001	0.05	-4.5751	-2.8272	
B	-2.5169	0.5002	-5.03	<.0001	0.05	-3.4973	-1.5365	
D	-3.3711	0.4207	-8.01	<.0001	0.05	-4.1957	-2.5465	
C	-1.1072	0.3113	-3.56	0.0004	0.05	-1.7174	-0.497	
A	-1.4044	0.2241	-6.27	<.0001	0.05	-1.8436	-0.9652	

Because the reference level "A" is included in the table, it seems to me that this lsmeans table is not comparing all levels of Bact to "Atr".

Differences of Bact Least Squares Means Adjustment for Multiple Comparisons: Bonferroni
Bact	_Bact	Estimate	Standard Error	z Value	Pr > |z|	Adj P	Alpha	Lower	Upper	Adj Lower	Adj Upper	Odds Ratio	Lower Confidence Limit for Odds Ratio	Upper Confidence Limit for Odds Ratio	Adj Lower Odds Ratio	Adj Upper  Odds Ratio	
E	B	-1.1843	0.6701	-1.77	0.0772	0.7717	0.05	-2.4976	0.1291	-3.0652	0.6967	0.306	0.082	1.138	0.047	2.007	
E	D	-0.33	0.6131	-0.54	0.5903	1	0.05	-1.5316	0.8715	-2.0509	1.3908	0.719	0.216	2.391	0.129	4.018	
E	C	-2.594	0.5438	-4.77	<.0001	<.0001	0.05	-3.6599	-1.5281	-4.1205	-1.0674	0.075	0.026	0.217	0.016	0.344	
E	A	-2.2967	0.4991	-4.6	<.0001	<.0001	0.05	-3.2749	-1.3186	-3.6976	-0.8959	0.101	0.038	0.268	0.025	0.408	
B	D	0.8542	0.6536	1.31	0.1912	1	0.05	-0.4268	2.1353	-0.9805	2.6889	2.35	0.653	8.459	0.375	14.716	
B	C	-1.4097	0.5892	-2.39	0.0167	0.1672	0.05	-2.5644	-0.255	-3.0635	0.2441	0.244	0.077	0.775	0.047	1.276	
B	A	-1.1125	0.5481	-2.03	0.0424	0.4239	0.05	-2.1867	-0.0382	-2.651	0.4261	0.329	0.112	0.963	0.071	1.531	
D	C	-2.2639	0.5234	-4.33	<.0001	0.0002	0.05	-3.2898	-1.2381	-3.7331	-0.7948	0.104	0.037	0.29	0.024	0.452	
D	A	-1.9667	0.4767	-4.13	<.0001	0.0004	0.05	-2.901	-1.0324	-3.3048	-0.6286	0.14	0.055	0.356	0.037	0.533	
C	A	0.2972	0.3836	0.77	0.4384	1	0.05	-0.4546	1.0491	-0.7795	1.374	1.346	0.635	2.855	0.459	3.951	

Each row of this table compares 2 levels levels of Bact at the time (pairwise comparisons).  For example,
1) E  vs B, in this order. E has a lower "infection succes rate" than B, but this difference is not significant (Adj P =0.7717).
2) B  vs D. B has a higher "infection success rate" than B, but again, thi  is not significant (Adj P=1).
3) B  vs C.   B has a lower "infection succes rate" than C, but this difference is not significant (Adj P=0.1672).
4) B vs A.   B has a lower "infection succes rate" than A, but this difference is not significant (Adj P=0.4239).
Th "raw mean " "infection succes rate" table below indicates that  B's  success is close to A's and C's success rate, so lines 3) and 4)  above make sense to me. 
What confuses me is that even though B's  success rate is far higher than that of  D's  and E's, the P values indicate that those differences  are not significant.  The conclussions in 1) and 2)  seem unexpected to me. 
Below is a table and a graph showing the "raw" "infection success rates" for Bact obtained from my experiments.
Bact	Sick	Raw Infection Success rate	
A	W	0.6889	
A	V	0.6667	
A	Z	0.3000	
A	X	0.0222	
A	Y	0.0111	
	Avg	0.3378	
			
B	W	0.8667	
B	Z	0.7667	
B	X	0.0000	
B	Y	0.0000	
B	V	0.0000	
	Avg	0.3267	
			
C	W	0.9222	
C	X	0.7000	
C	Y	0.1889	
C	Mojave	0.1000	
C	V	0.0000	
	Avg	0.3822	
			
D	X	0.1111	
D	W	0.1000	
D	Y	0.0889	
D	Z	0.0000	
D	V	0.0000	
	Avg	0.0600	
			
E	X	0.1444	
E	W	0.0889	
E	Y	0.0111	
E	Z	0.0000	
E	V	0.0000	
	Avg	0.0489	

I used this note https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect074.htm to explore the use of lsmeans.
Perhaps the reason for the conclussions I stated in lines 1), 2), 3) and 4)  (if they are correct)  is explained in this sentence  "... This odds ratio is computed at an average of the interacting effects ...",  which I fail to fully understand. Please, can you with this issue?

The highligthed quote above refers to a table whose equivalent for my data is shown below:
Coefficients for Bact Least Squares Means
Parameter	Sick	Bact	Row1	Row2	Row3	Row4	Row5	
Intercept			1	1	1	1	1	
Sick Z	Z		0.2	0.2	0.2	0.2	0.2	
Sick W	W		0.2	0.2	0.2	0.2	0.2	
Sick Y	Y		0.2	0.2	0.2	0.2	0.2	
Sick X	X		0.2	0.2	0.2	0.2	0.2	
Sick V	V		0.2	0.2	0.2	0.2	0.2	
Bact E		E	1					
Bact B		B		1				
Bact D		D			1			
Bact C		C				1		
Bact A		A					1	
Sick Z * Bact E	Z	E	0.2					
Sick Z * Bact B	Z	B		0.2				
Sick Z * Bact D	Z	D			0.2			
Sick Z * Bact C	Z	C				0.2		
Sick Z * Bact A	Z	A					0.2	
Sick W * Bact E	W	E	0.2					
Sick W * Bact B	W	B		0.2				
Sick W * Bact D	W	D			0.2			
Sick W * Bact C	W	C				0.2		
Sick W * Bact A	W	A					0.2	
Sick Y* Bact E	Y	E	0.2					
Sick Y * Bact B	Y	B		0.2				
Sick Y * Bact D	Y	D			0.2			
Sick Y * Bact C	Y	C				0.2		
Sick Y * Bact A	Y	A					0.2	
Sick X * Bact E	X	E	0.2					
Sick X * Bact B	X	B		0.2				
Sick X * Bact D	X	D			0.2			
Sick X * Bact C	X	C				0.2		
Sick X * Bact A	X	A					0.2	
Sick V * Bact E	V	E	0.2					
Sick V * Bact B	V	B		0.2				
Sick V * Bact D	V	D			0.2			
Sick V * Bact C	V	C				0.2		
Sick V * Bact A	V	A					0.2