Are people living with HIV less productive at work?

Figures & data

Table 1. Baseline characteristics of our sample.

Type of variable	Variable	Measure	PLWH (n = 298)	Working PLWH (n = 188)	General population (n = 986)
Demographic	Gender (Female = 1)	%	13.4	11.7	48.8
	Age	Mean (SD)	46.9 (9.7)	45.9 (8.5)	41.3 (12.3)
Socio-economic	Education Low	%	21.8	17.6	25.4
	Education Middle	%	33.9	35.6	42.8
	Education High	%	44.3	46.9	31.8
	Work hours	Mean (SD)	22.7 (17.9)	35.9 (5.9)	32.6 (9.3)
	Income after taxes	€ (SD)	–	2,342.59 (1,088.03)	1,877.91 (1,405.78)
Health	Health^a	Mean (SD)	1.24 (1.41)	0.78 (1.08)	0.64 (1.07)
	Months since diagnosis	Mean (SD)	91.9 (78.5)	79.5 (69.1)	NA
	Cognitive problems	%	0.41	0.31	NA
	Partner	%	51.3	58.0	74.7
	Single	%	48.7	42.0	25.3
	CD4	Mean (SD)	0.64 (0.33)^b	0.63 (0.27)^c	NA
	CD4Nadir	Mean (SD)	0.26 (0.17)^b	0.26 (0.17)^c	NA
	Viral Load	Mean (SD)	2.52 (1.58)^d	2.66 (1.78)	NA

^aMisery index; range 0–10. ^bn = 296. ^cn = 187. ^dn = 297.

Table 2. Productivity losses in the past 4 weeks.

Variable	Measure	PLWH employed (n = 188)	General population (n = 986)
Absenteeism	%	22.3	26.2
Presenteeism	%	27.1	20.7
Absenteeism and presenteeism	%	10.6	13.7
Days of absenteeism	Mean (SD; range)	11.05 (19.86; 1–20)	5.31 (4.39; 1–20)
Days of presenteeism	Mean (SD; range)	8.56 (6.20; 1–20)	6.10 (4.84; 1–20)
Quality of work in presenteeism	Mean (SD; range)	0.75 (0.19; 0–1)	0.45 (0.19; 0–1)
Productivity losses (hours, per person)	Mean (SD; range)	40.11 (42.97; 0–160)	38.6 (40.54; 0–228)
Productivity losses (hours, total)		2,928.36	12,621.29
Productivity costs (€, per person)	Mean (SD; range)	649.54 (823.38; 0–3,399.6)	511.7 (616.6; 0–3,749.5)
Productivity costs (€, total)		47,416	167,332

Note: Not all have productivity losses (presenteeism of X days). 4 missing values “days of absenteeism” -> said to have absenteeism but not how many days. 1 missing values “days of presenteeism” -> said to have presenteeism but not how many days. Assumption: hours per week/5. Rescaling for > 20 days and those with over 60 h, assign 60 h. For income we used Use Multiple Imputation for 16 missing cases (out of 192); we used education, gender and age.

Table 3. Correlations of independent variables with productivity losses (in hours) in aggregated working sample.

	Total sample (n = 1,177)	Sub-sample with absenteeism and/or presenteeism (n = 400)
HIV	0.03	0.01
Gender [female = 1]	−0.03	−0.16**
Age	0.02	0.21***
Education low	0.03	0.11**
Education middle	0.01	−0.08
Education high	−0.03	−0.01
Partner [yes = 1]	−0.03	−0.04
Health	0.38***	0.26***

Note: significance level of p-value ***p < 0.001 **p < 0.01 *p < 0.05.

Table 4. The occurrence and scope of productivity losses in the working PLWH sample.

	Absenteeism^a (logit model)		Presenteeism^b (logit model)		Productivity losses (two-part model)
					Part 1^c (logit)		Part 2^d (log-OLS)
	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.
Intercept	−10.690*	5.132	2.085	4.144	−6.409*	2.892	−9.248*	4.034
Gender [female = 1]	0.655	0.616	0.476	0.587	0.213	0.435	0.747	0.563
Age	0.438	0.231	−0.225	0.188	0.229	0.131	0.566**	0.190
Age (squared)	−0.005	0.003	0.002	0.002	−0.003*	0.001	−0.006**	0.002
Education middle	−0.682	0.587	0.376	0.637	0.593	0.438	−0.933	0.605
Education high	−0.393	0.540	0.667	0.615	0.766	0.422	−0.929	0.547
Partner [yes = 1]	−0.309	0.389	0.486	0.400	0.380	0.303	−0.412	0.351
Health	0.948**	0.342	0.993**	0.365	0.357	0.282	0.969**	0.302
Health (squared)	−0.072	0.061	−0.056	0.079	−0.039	0.057	−0.150**	0.045
Cognitive problems [yes = 1]	−0.153	0.451	0.761	0.430	0.260	0.346	−0.424	0.390
Months since diagnosis	−0.001	0.010	0.008	0.010	−0.001	0.006	0.003	0.009
Months since diagnosis (squared)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Note: Significance level of p-value ***p < 0.001 **p < 0.01 *p < 0.05.^aabsenteeism yes = 1, no = 0. ^bpresenteeism yes = 1, no = 0. ^cproductivity losses (absenteeism and/or presenteeism) yes = 1, no = 0. ^dproductivity losses in hours (if absenteeism and/or presenteeism = yes).

Table 5. The occurrence and scope of productivity losses in the aggregated working sample (PLWH and GWP).

	Absenteeism^a (logit model)		Presenteeism^b (logit model)		Productivity losses (two-part model)
					Part 1^c (logit)		Part 2^d (Gamma log-link)
	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.
Intercept	0.855	−1.587	0.963	−1.854	−0.787	0.828	3.781***	0.616
Working PLWH sample [yes = 1]	−0.214	0.215	0.518*	0.220	0.345	0.196	−0.183	0.145
Gender [female = 1]	0.073	0.151	0.209	0.171	0.107	0.146	−0.246*	0.108
Age	0.013	0.044	−0.009	0.049	0.000	0.042	−0.011	0.032
Age (squared)	0.000	0.001	0.000	0.001	0.000	0.001	0.000	0.000
Education middle	0.184	0.187	0.008	0.204	0.151	0.178	−0.205	0.135
Education high	0.071	0.198	−0.117	0.218	−0.063	0.188	−0.193	0.144
Partner [yes = 1]	−0.035	0.160	0.192	0.181	0.057	0.154	−0.116	0.114
Health	0.932**	0.136	1.280**	0.141	1.147**	0.136	0.272**	0.084
Health (squared)	−0.093**	0.034	−0.115**	0.033	−0.095**	0.035	−0.032	0.018

Note: significance level of p-value ***p < 0.001 **p < 0.01 *p < 0.05. ^aabsenteeism yes = 1, no = 0. ^bpresenteeism yes = 1, no = 0. ^cproductivity losses (absenteeism and/or presenteeism) yes = 1, no = 0. ^dproductivity losses in hours (if absenteeism and/or presenteeism = yes).