Datasets:

electricsheepafrica
/

africa-synth-laboratory-quality-management-all

id int64	facility_level string	location string	lab_sections int64	iso_15189_accredited int64	slipta_enrolled int64	slipta_stars int64	slmta_trained_staff int64	quality_manual int64	sop_available int64	sop_current int64	document_control int64	eqa_enrolled int64	eqa_acceptable_score int64	internal_audit_done int64	corrective_action_system int64	nonconformity_logged int64	management_review int64	staff_competency_assessed int64	qualified_lab_staff int64	staff_to_workload_adequate int64	continuing_education int64	equipment_maintenance_plan int64	equipment_calibrated int64	equipment_downtime_days_month int64	temperature_monitored int64	reagent_stockout_past_month int64	specimen_collection_sop int64	specimen_rejection_rate_pct float64	pre_analytical_errors_pct float64	patient_id_verified int64	tat_within_target int64	critical_value_notification int64	result_verification int64	biosafety_compliant int64	waste_management_compliant int64	quality_score_pct float64	year int64
1	health_centre	peri_urban	5	0	1	0	2	0	1	0	0	0	0	0	0	0	1	0	2	0	0	0	1	7	0	1	1	11.7	28.3	1	0	0	0	0	0	20	2,023
2	health_centre	peri_urban	2	0	1	1	0	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0	5	1	1	0	12.4	40.5	0	0	0	0	0	1	10	2,019
3	regional_hospital	rural	3	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	8	1	0	0	1	10	0	1	0	16.1	41.7	1	1	0	1	0	0	30	2,021
4	health_centre	urban	6	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	0	12.8	49.1	0	1	0	1	1	0	20	2,021
5	health_centre	rural	2	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	2	0	1	0	0	7	1	1	1	13.9	31.8	0	1	1	1	1	0	50	2,020
6	health_centre	rural	3	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	2	0	0	1	1	5	0	1	0	15.6	29.2	0	1	0	0	0	0	30	2,021
7	district_hospital	peri_urban	4	0	0	0	0	1	1	0	0	1	1	0	1	0	0	1	1	0	0	1	1	0	1	1	1	13.4	43.1	0	1	0	0	0	1	60	2,020
8	district_hospital	urban	3	0	1	0	1	0	1	0	0	0	0	0	0	0	0	1	3	0	0	1	1	2	0	1	1	11	34.5	0	1	0	0	0	0	40	2,022
9	health_centre	rural	4	0	0	0	0	1	0	0	1	0	0	0	1	0	1	0	1	0	0	0	0	9	0	0	0	12.9	35.6	0	0	0	0	1	1	10	2,019
10	health_centre	peri_urban	4	0	1	1	1	0	1	1	1	0	0	0	0	0	0	0	2	0	0	0	1	5	0	0	1	8.2	22.2	0	1	0	0	1	0	40	2,023
11	district_hospital	rural	1	0	0	0	0	0	1	0	1	1	1	0	0	0	0	1	1	1	0	0	1	8	1	0	1	16.1	35.7	0	1	0	1	0	0	60	2,023
12	health_centre	urban	3	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	2	0	0	0	1	6	0	0	1	11.9	37.5	0	1	0	0	1	0	50	2,021
13	district_hospital	peri_urban	3	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	1	4	1	1	1	6.5	32.3	0	0	0	1	0	1	30	2,021
14	private_lab	rural	4	0	0	0	0	0	1	0	1	1	1	1	0	0	0	0	2	0	0	1	0	2	1	0	1	13.4	22.5	1	0	0	0	1	0	50	2,023
15	health_centre	rural	4	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	1	5	0	1	0	12.8	27.8	0	0	0	0	0	0	10	2,022
16	private_lab	rural	5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	9	0	1	0	8.9	36.9	0	0	0	0	0	1	0	2,020
17	health_centre	rural	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	2	0	0	1	0	4	1	0	0	6.8	36.3	0	0	1	0	1	1	30	2,019
18	district_hospital	rural	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	1	0	2	0	1	0	13.9	45	0	0	0	0	1	0	10	2,019
19	health_centre	rural	3	0	1	0	0	1	1	1	0	0	0	0	1	1	0	1	1	0	0	0	1	2	0	0	1	7.3	40.5	0	0	0	0	0	0	30	2,022
20	district_hospital	urban	5	0	0	0	0	0	1	1	1	0	0	1	0	1	1	0	1	0	0	1	0	1	0	0	1	12	32.6	0	0	0	0	0	0	20	2,023
21	health_centre	urban	3	0	1	1	0	1	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	2	0	0	1	11.4	26.3	1	1	0	0	0	0	40	2,019
22	health_centre	urban	5	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	2	1	0	0	0	8	0	1	1	11.6	44.7	0	0	0	1	0	0	20	2,023
23	regional_hospital	urban	4	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	5	1	0	0	0	6	0	0	0	11.9	37.2	0	0	0	0	1	0	20	2,020
24	health_centre	rural	5	0	0	0	0	0	1	1	0	0	0	1	0	0	0	1	1	0	0	0	0	8	1	0	1	9.7	38.2	0	1	0	0	0	0	50	2,022
25	district_hospital	urban	2	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	2	0	0	1	1	0	0	1	0	13.5	44.6	0	0	1	0	0	0	20	2,021
26	regional_hospital	rural	2	1	1	2	5	0	1	0	0	1	1	1	0	0	0	0	7	0	0	0	1	15	0	1	1	10.8	40.1	1	1	0	0	0	1	60	2,021
27	regional_hospital	rural	5	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	6	1	0	0	0	16	0	1	1	12.4	38.3	1	0	0	1	0	0	10	2,020
28	national_reference	urban	4	0	1	2	1	0	1	1	0	0	0	0	0	1	1	0	5	0	0	0	0	10	0	1	1	14.1	24.2	0	0	0	0	0	0	10	2,023
29	regional_hospital	rural	1	0	0	0	0	1	1	1	0	0	0	1	0	0	0	1	6	0	0	0	0	6	1	1	1	15.1	36.5	0	1	0	0	0	0	50	2,023
30	private_lab	urban	2	0	1	0	1	0	0	0	1	1	1	0	0	0	0	0	1	0	0	0	0	2	1	0	0	13.1	39.9	0	0	0	0	1	0	30	2,022
31	regional_hospital	rural	2	0	1	2	1	0	1	1	1	0	0	0	0	0	0	0	1	1	0	1	0	4	0	1	1	10.3	39.5	0	1	0	0	0	0	20	2,021
32	district_hospital	peri_urban	1	0	1	2	2	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	1	11	0	1	0	13.1	30	1	1	0	0	1	0	30	2,023
33	district_hospital	peri_urban	1	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	2	0	0	0	0	8	0	1	1	13.1	23.1	0	1	0	0	0	1	20	2,021
34	health_centre	urban	1	0	1	0	2	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	1	8	0	1	1	14.7	21.1	1	1	0	0	1	0	40	2,022
35	health_centre	rural	4	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	4	0	0	0	7.8	44.5	1	1	1	1	0	0	30	2,022
36	health_centre	urban	4	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	1	0	0	1	0	2	1	0	1	12.7	34.3	0	0	0	0	0	1	30	2,022
37	district_hospital	rural	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	8	0	0	0	11.4	33.7	1	0	1	1	0	0	10	2,023
38	health_centre	rural	2	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	3	0	0	0	0	6	0	1	1	12.6	29.3	0	0	0	0	0	0	10	2,023
39	health_centre	peri_urban	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	4	0	0	0	0	4	0	1	0	7.9	30	1	1	0	1	0	1	20	2,022
40	health_centre	urban	2	0	1	1	1	0	0	0	0	0	0	0	1	0	0	1	1	0	0	1	0	2	1	1	0	7.8	23.7	0	0	0	0	0	0	20	2,020
41	health_centre	rural	1	0	1	0	1	0	0	0	0	1	1	0	1	0	0	1	2	1	0	0	1	9	0	0	0	6.7	42.8	0	0	0	0	0	0	30	2,019
42	district_hospital	peri_urban	3	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	3	0	0	1	0	3	0	1	0	15	37.9	0	1	0	0	0	0	20	2,019
43	district_hospital	rural	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	10	0	0	0	10.2	30.8	1	1	1	1	1	0	30	2,019
44	regional_hospital	rural	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	8	0	0	0	18.8	26.8	1	0	0	0	1	0	10	2,021
45	regional_hospital	urban	3	0	1	2	6	0	0	0	0	1	1	0	0	0	0	0	3	1	0	0	0	5	1	0	0	10.8	35.7	1	0	0	0	0	0	20	2,020
46	district_hospital	urban	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	5	0	0	0	0	8	1	0	0	8.6	31.9	1	1	0	1	1	1	30	2,022
47	national_reference	rural	2	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	6	0	1	0	0	7	0	0	1	5.5	32.9	1	0	0	1	0	0	20	2,020
48	private_lab	urban	5	0	1	1	4	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0	9	0	0	0	12.3	25.2	0	1	1	0	0	1	20	2,021
49	health_centre	urban	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0	6	1	1	0	13.5	36.4	0	0	0	0	1	0	40	2,021
50	district_hospital	urban	4	0	1	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	7	0	0	0	12	28.7	1	0	0	0	0	0	10	2,021
51	health_centre	rural	2	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	1	12.2	39.7	0	0	0	0	0	0	20	2,020
52	health_centre	peri_urban	2	0	1	1	0	1	1	1	0	1	1	1	0	0	0	0	1	1	0	0	0	13	0	0	1	6.9	29.3	1	0	0	0	0	1	30	2,020
53	district_hospital	urban	3	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	8	0	0	0	9.9	27.5	0	1	0	1	0	1	10	2,023
54	private_lab	urban	2	0	0	0	0	0	1	1	0	0	0	0	0	0	1	1	3	0	0	1	0	2	1	0	1	18.4	36.9	1	0	0	0	1	0	40	2,021
55	health_centre	urban	1	0	1	1	0	1	0	0	0	1	1	0	0	0	0	0	2	0	0	0	0	12	0	1	0	18.6	37.2	0	1	0	0	1	1	30	2,023
56	health_centre	urban	2	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	2	0	0	0	0	7	0	0	1	10.6	38.3	1	0	1	1	0	1	30	2,020
57	health_centre	peri_urban	3	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	2	0	1	0	10.7	38	0	0	0	0	1	0	20	2,019
58	district_hospital	rural	4	0	1	1	0	0	0	0	0	1	1	0	1	0	0	1	2	1	1	1	0	1	0	1	0	16.4	33.5	0	1	0	0	0	1	30	2,020
59	health_centre	peri_urban	4	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	1	0	1	16.8	32.2	0	1	0	0	0	0	40	2,022
60	health_centre	urban	4	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	11	0	1	0	7.7	43.9	0	0	0	0	0	0	10	2,022
61	district_hospital	urban	1	0	1	0	1	1	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	8	0	0	1	10.8	35.7	0	0	1	1	1	0	30	2,020
62	district_hospital	peri_urban	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	5	0	1	0	14.4	29.4	1	1	0	0	0	0	20	2,020
63	health_centre	urban	2	0	1	2	2	0	1	1	1	0	0	0	1	0	0	0	3	1	0	1	0	0	1	1	1	10.9	43.7	1	0	0	0	0	0	20	2,020
64	district_hospital	rural	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	2	0	0	0	0	13	1	1	0	16.4	36.5	0	0	0	0	1	0	30	2,020
65	health_centre	rural	3	0	1	0	3	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	2	0	0	0	14.5	30.2	0	0	0	0	0	0	10	2,022
66	district_hospital	rural	5	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	2	0	1	0	0	11	1	1	0	15.9	28.4	0	0	1	0	0	0	20	2,021
67	district_hospital	urban	4	0	1	0	1	1	0	0	0	1	1	1	0	0	0	1	1	0	0	0	1	6	0	0	0	16.2	42.6	0	0	0	0	1	0	50	2,019
68	private_lab	peri_urban	4	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	1	1	0	0	1	13	0	0	0	16.2	28.2	0	1	0	0	0	1	40	2,020
69	national_reference	rural	4	1	1	1	4	0	1	0	0	0	0	0	0	0	0	1	7	1	0	0	0	6	0	1	1	10.7	46.8	0	0	0	0	0	0	30	2,021
70	health_centre	urban	1	0	1	1	2	0	0	0	1	1	1	0	0	0	0	1	1	1	0	0	0	4	1	0	0	17.8	21.7	0	1	0	1	0	0	40	2,021
71	private_lab	urban	6	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	1	1	0	0	1	8	1	0	1	12.2	39.6	0	1	1	1	1	0	80	2,020
72	health_centre	urban	2	0	1	1	2	0	1	0	0	1	1	1	0	0	0	0	1	0	0	0	0	9	0	0	1	12	17.1	1	1	0	1	0	1	40	2,022
73	regional_hospital	rural	4	0	1	3	3	0	1	0	0	1	0	1	0	0	1	1	8	0	0	0	0	9	1	1	1	16.2	29.5	0	1	1	0	0	0	70	2,023
74	district_hospital	rural	6	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	7	0	0	1	17.9	22.6	0	1	0	0	0	0	30	2,019
75	health_centre	rural	4	0	1	0	4	0	0	0	1	1	1	0	0	0	0	0	3	0	0	0	0	8	1	1	0	14.7	46.4	1	1	0	1	0	0	30	2,021
76	private_lab	urban	1	0	0	0	0	0	1	0	0	1	1	0	1	0	0	0	2	1	0	0	1	3	0	1	1	8.5	27.8	1	0	0	0	1	1	40	2,019
77	health_centre	rural	4	0	0	0	0	0	1	0	0	1	1	0	0	0	0	1	1	0	0	1	0	1	0	0	1	13	36.5	0	1	1	0	0	0	50	2,023
78	health_centre	rural	3	1	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	1	8	1	1	0	13.6	39.4	0	1	0	0	0	0	50	2,019
79	health_centre	urban	4	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	1	0	1	1	0	0	8.5	34.1	0	0	0	0	0	0	20	2,021
80	health_centre	urban	1	0	1	1	0	1	0	0	0	0	0	1	0	0	0	0	3	0	0	1	0	1	0	0	0	13.8	23.4	1	0	1	0	0	0	20	2,022
81	district_hospital	urban	4	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	2	0	0	0	0	7	0	0	0	15.2	32.4	0	1	1	1	0	0	30	2,021
82	health_centre	peri_urban	4	0	1	0	1	1	1	0	0	1	1	0	1	0	0	0	1	0	0	0	0	12	0	0	1	14.3	22.1	0	0	1	0	0	0	30	2,023
83	health_centre	peri_urban	2	0	1	0	0	0	1	1	1	0	0	0	1	0	0	0	2	0	1	0	0	8	1	0	1	12.6	27.6	1	0	0	0	0	0	20	2,021
84	health_centre	rural	3	1	1	1	3	0	0	0	0	1	1	0	0	0	0	0	2	0	0	0	0	15	0	0	0	12.4	32.1	0	0	0	0	0	0	20	2,019
85	health_centre	rural	3	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	2	0	1	0	0	5	1	0	1	14.1	50.5	0	1	0	1	0	0	40	2,022
86	private_lab	urban	2	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	3	0	0	1	0	4	0	1	1	8.4	27.4	0	0	0	1	0	1	10	2,023
87	health_centre	peri_urban	4	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	4	1	0	1	0	0	1	1	0	9.2	47.5	0	1	0	0	0	0	30	2,021
88	regional_hospital	urban	6	0	1	3	6	1	0	0	0	0	0	0	1	0	0	0	2	0	0	1	0	2	0	1	0	8.4	38.9	1	1	1	1	0	0	20	2,019
89	district_hospital	rural	5	0	1	1	0	1	1	0	0	0	0	0	0	0	0	0	1	1	0	1	0	1	1	1	1	7.8	46.7	0	1	0	1	0	0	30	2,021
90	district_hospital	urban	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	7	0	0	0	0	14	0	1	0	14.2	40	0	0	1	0	0	0	20	2,021
91	district_hospital	urban	3	0	0	0	0	0	1	1	0	0	0	1	0	0	1	1	1	0	0	0	0	13	1	0	1	8.8	25.2	1	1	0	1	0	1	50	2,020
92	health_centre	rural	3	0	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	0	0	0	0	9	0	0	1	13.9	57.4	0	0	0	0	0	1	30	2,023
93	district_hospital	rural	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	1	7	0	0	0	6.4	27.4	1	1	0	0	1	0	30	2,019
94	district_hospital	rural	5	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	2	0	0	1	1	3	0	0	1	11.8	36.5	0	0	0	0	0	0	20	2,021
95	health_centre	peri_urban	4	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	4	0	1	1	12.3	20	1	0	0	0	0	0	20	2,022
96	regional_hospital	peri_urban	6	0	1	3	1	0	1	1	1	0	0	1	0	0	0	1	2	0	1	1	0	1	0	0	1	7.8	16.3	1	1	0	0	0	0	40	2,021
97	regional_hospital	urban	2	1	1	5	2	0	0	0	0	0	0	1	0	0	0	0	3	0	0	1	0	0	0	1	0	9.7	26.5	0	0	0	0	0	0	20	2,023
98	health_centre	peri_urban	3	0	1	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	11	0	1	1	17.1	29.7	1	0	0	0	1	1	40	2,021
99	health_centre	peri_urban	2	0	1	1	1	0	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0	12	0	0	0	12	36.6	1	0	0	0	0	0	10	2,023
100	private_lab	peri_urban	6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	4	0	1	0	0	5	0	1	0	14.1	39.7	1	0	1	0	1	0	30	2,020

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Laboratory Quality Management

Abstract

Synthetic dataset modeling laboratory quality management systems across three SSA scenarios. Captures ISO 15189 accreditation, SLIPTA stars, EQA participation, SOPs, equipment maintenance, pre-analytical errors, turnaround times, and composite quality scores. Parameterized from primary SSA laboratory quality research.

Parameterization Evidence

Parameter	Value	Source	Year
Accredited labs by 2020	668 (75% increase from 2013)	Ondoa et al. Trop Med Int Health	2022
SA share of accredited labs	55% (396/668)	Ondoa et al.	2022
SLIPTA→ISO 15189 rate	19% within 24 months	Ondoa et al.	2022
Lower-tier accreditation	7.7% vs 30% higher-tier	Ondoa et al.	2022
SLMTA reach	617 labs in 47 countries	Yao et al. Afr J Lab Med	2014
SSA labs accredited	~1%	Nkengasong et al. Lancet	2018
Pre-analytical errors	46-68% of all lab errors	Alemnji et al. Glob Public Health	2014

Validation

Usage

from datasets import load_dataset
ds = load_dataset("electricsheepafrica/laboratory-quality-management", name="slmta_in_progress")
df = ds['train'].to_pandas()

References

Ondoa P et al. (2022). 7 years QMS in SSA labs. Trop Med Int Health. DOI: 10.1111/tmi.13839
Yao K et al. (2014). SLMTA programme. Afr J Lab Med. DOI: 10.4102/ajlm.v3i2.194
Nkengasong JN et al. (2018). Laboratory medicine Africa. Lancet. DOI: 10.1016/S0140-6736(18)30625-8
Alemnji G et al. (2014). Strengthening lab systems. Glob Public Health

License

CC-BY-4.0

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