DOI: https://doi.org/10.21203/rs.3.rs-23855/v3
Background: General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17.
Methods: We undertook input-oriented Data Envelopment Analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1.
Results: The Average Constant Returns to Scale, Variable Returns to Scale and Scale Efficiency of general hospitals for 2016/17 were 49% (95% CI, 44% - 54%), 69% (95% CI, 65% - 74%) and 70% (95% CI, 65% - 75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.
Conclusion: The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.
This preprint is available for download as a PDF.
Table 1: Number of health facilities in Uganda disaggregated by level of care
Facility level |
Count |
Clinics |
1,578 (22.75%) |
Level II Health Center (HCII) |
3,364 (48.49%) |
Level III Health Center (HCIII) |
1,569 (22.62%) |
Level IV Health Center (HCIV) |
222 (3.2%) |
General hospitals |
163 (2.35%) |
Regional referral hospitals |
13 (0.19%) |
Referral hospitals |
3 (0.04%) |
National referral hospitals |
2 (0.03%) |
Specialized hospitals |
23 (0.33%) |
Source: Ministry of Health. Facility Master List, 2018
Table 2: Study variables and data sources
Variable |
Definition |
Measurement |
Source (s) of data |
Inputs |
|
|
|
Bed |
Hospital beds |
Total number of beds in the year |
AHSPR for FYs 2012/13, 2014/15 and 2016/17 |
Staff |
Medical personnel |
Total number of staff (Medical Officers, Dental, Pharmacy, Nursing, Allied Health Professionals, Administrative and Other Staff) in the year |
AHSPR (FY 2016/17), Integrated Human Resource Information System; Reports of the Catholic and Protestant Medical Bureaux |
Outputs |
|
|
|
OPD |
OPD visits |
Total number of outpatient visits in the year |
AHSPR for FYs 2012/13, 2014/15 and 2016/17 |
ADM |
Hospital admissions |
Total number of inpatient admissions |
AHSPR for FYs 2012/13, 2014/15 and 2016/17 |
Deliveries |
Deliveries (births) |
Total number of deliveries in the year |
AHSPR for FYs 2012/13, 2014/15 and 2016/17 |
Predictors |
|
|
|
Ownership |
Hospital ownership |
Authority that owns the hospital: public (1) or private (0) |
AHSPR (FY 2016/17) |
Hospsize |
Hospital size |
Size of the hospital classified using the median number of beds: large (>120 beds [1]), small (<=120 beds [0]). Given the variability in sizes of general hospitals across the world, lack of global or national benchmark for their optimal size and the need to ensure fair distribution of small and large hospitals presentation in the 2 groups, the authors used the median bed size rounded to the nearest ten i.e. 120 as the benchmark to classify the 78 general hospitals as small and large. |
PR (FY 2016/17) |
Propqualstaff |
Proportion of qualified staff |
Number of staff with formal qualifications (Medical Officers, Dental, Pharmacy, Nursing, Allied Health Professionals, Administrative and Other Staff) as a proportion of all staff in the year |
iHRIS, Reports from Catholic and Protestant Medical Bureaus |
Region |
Geographical location |
Region where the hospital is located: Central or Western Uganda (1), Northern or Eastern Uganda (0) |
AHSPR (FY 2016/17) |
BOR |
Bed occupancy rate |
Total annual inpatient days as a ratio of annual available bed days ×100 |
AHSPR (FY 2016/17) |
TrainingStatus |
Training status |
Hospital is used for training health professionals or not: Yes (1) and No (0) |
Ministry of Health Training Unit, Catholic Medical and Protestant Medical Bureaus |
OPDIBD |
Outpatient visit to total inpatient days ratio |
Total number of OPD visits divided by total number of inpatient bed days in the year |
AHSPR (FY 2016/17) |
AvStayADM |
Average length of stay |
Total annual number of inpatient days spent/total annual number of admissions |
AHSPR (FY 2016/17) |
Table 3: Descriptive statistics for input and output variables
Group |
Variable |
obs |
Mean |
Std. Dev |
Min |
Max |
A - All hospitals |
Beds |
78 |
142 |
58.75402 |
61 |
305 |
Staff |
78 |
146 |
67.14357 |
42 |
433 |
|
OPD |
78 |
38720 |
27127.02 |
4873 |
178146 |
|
ADM |
78 |
8,948 |
4540.011 |
1427 |
23560 |
|
Deliveries |
78 |
2,127 |
1438.253 |
229 |
7002 |
|
B – Public hospitals |
Beds |
40 |
126 |
38.33315 |
76 |
224 |
Staff |
40 |
135 |
28.97035 |
81 |
204 |
|
OPD |
40 |
53,562 |
28703.54 |
18790 |
178146 |
|
ADM |
40 |
10,972 |
3882.417 |
3885 |
23560 |
|
Deliveries |
40 |
2,776 |
1637.149 |
544 |
7002 |
|
C – PNFP hospitals |
Beds |
38 |
159 |
70.99094 |
61 |
305 |
Staff |
38 |
159 |
90.5376 |
42 |
433 |
|
OPD |
38 |
23,097 |
13197.76 |
4873 |
64580 |
|
ADM |
38 |
6,817 |
4231.765 |
1427 |
20446 |
|
Deliveries |
38 |
1,444 |
738.1052 |
229 |
3453 |
Table 4: Descriptive statistics for the continuous independent variables
Variable |
obs |
Mean |
Std. Dev |
Min |
Max |
Propqualstaff |
78 |
76.05128 |
12.38652 |
43 |
100 |
BOR |
78 |
66.61538 |
36.18503 |
15 |
178 |
OPDIBD |
78 |
1.371795 |
0.9109575 |
0.2 |
4.5 |
AvStayADM |
78 |
3.871795 |
1.399063 |
2 |
8 |
Table 5: Descriptive statistics for the categorical independent variables
Variable |
Coding |
Frequency |
Percent |
Cumulative % |
Ownership |
1, Public |
40 |
51.28 |
51.28 |
0, PNFP |
38 |
48.72 |
100 |
|
Hospital size |
1, Big (>120 beds) |
37 |
47.44 |
47.44 |
0, Small (<= 120 beds) |
41 |
52.56 |
100 |
|
Geographical location |
1, Central or Western |
42 |
53.85 |
53.85 |
0, Northern or Eastern |
36 |
46.15 |
100 |
|
Training status |
1, Yes |
28 |
35.90 |
35.90 |
0, No |
50 |
64.10 |
100 |
Table 6: Hospital efficiency scores disaggregated by hospital ownership during FY 2016/17
Parameter in separate groups of hospitals |
CRS TE 2016/17 |
VRS TE 2016/17 |
SE 2016/17 |
|||
All hospitals |
|
|
|
|||
Number of efficient hospitals |
2 |
8 |
2 |
|||
Number of inefficient Hospitals |
76 |
70 |
76 |
|||
Efficient hospitals (%) |
3 |
11 |
3 |
|||
Inefficient hospitals (%) |
97 |
90 |
97 |
|||
Average efficiency score (%) |
49 |
69 |
70 |
|||
Minimum score (%) |
13 |
25 |
18 |
|||
Maximum score (%) |
100 |
100 |
100 |
|||
Public hospitals |
|
|
|
|||
Number of efficient hospitals |
2 |
6 |
2 |
|||
Number of inefficient hospitals |
38 |
34 |
38 |
|||
Efficient hospitals (%) |
5 |
15 |
5 |
|||
Inefficient hospitals (%) |
95 |
85 |
95 |
|||
Average efficiency score (%) |
64 |
82 |
78 |
|||
Minimum score (%) |
28 |
50 |
45 |
|||
Maximum score (%) |
100 |
100 |
100 |
|||
PNFP hospitals |
|
|
|
|||
Number of efficient hospitals |
10 |
16 |
10 |
|||
Number of inefficient hospitals |
28 |
22 |
28 |
|||
Efficient hospitals (%) |
26 |
42 |
26 |
|||
Inefficient hospitals (%) |
74 |
58 |
74 |
|||
Average efficiency score (%) |
73 |
83 |
87 |
|||
Minimum score (%) |
28 |
43 |
30 |
|||
Maximum score (%) |
100 |
100 |
100 |
|||
Table 7: Top 10 hospitals in FYs 2012/13, FY 2014/15 and FY 2016/17
FY 2012/13 |
FY 2014/15 |
FY 2016/17 |
||||
SN |
Hospital |
SE Score (Super-effCRS) |
Hospital |
SE Score (Super-effCRS) |
Hospital |
SE Score (Super-effCRS) |
1 |
Iganga |
1.0000 (2.0224) |
Iganga |
1.0000 (1.800) |
Iganga |
1.0000(1.977) |
2 |
Busolwe |
1.0000(1.4456) |
Busolwe |
1.0000 (1.373) |
Tororo |
1.0000 (1.112) |
3 |
Bwera |
1.0000 (1.2163) |
Mityana |
1.0000 (1.087) |
Kalongo |
0.9956 |
4 |
Mityana |
1.0000 (1.0771) |
Kagadi |
1.0000 (1.027) |
Kitgum |
0.9946 |
5 |
Masafu |
1.0000 (1.0768) |
Pallisa |
1.0000 (1.016) |
Mityana |
0.9818 |
6 |
Tororo |
0.9910 |
Ibanda |
0.9958 |
Angal St. Luke |
0.9795 |
7 |
Kitagata |
0.9906 |
Tororo |
0.9926 |
Bududa |
0.9759 |
8 |
Moyo |
0.9896 |
Kitgum |
0.9888 |
Atutur |
0.9752 |
9 |
Ibanda |
0.9884 |
Angal St. Luke |
0.9880 |
Entebbe |
0.9661 |
10 |
Entebbe |
0.9817 |
Nebbi |
0.9744 |
Ibanda |
0.9584 |
Table 8: Bottom 10 hospitals in FYs 2012/13, FY 2014/15 and FY 2016/17
FY 2012/13 |
FY 2014/15 |
FY 2016/17 |
||||
SN |
Hospital |
CRS TE |
Hospital |
CRS TE |
Hospital |
CRS TE |
1 |
Matany |
0.19 |
Aber |
0.43 |
St. Francis Nyenga |
0.13 |
2 |
Maracha |
0.21 |
Abim |
0.24 |
Amai Community |
0.16 |
3 |
St. Francis Nyenga |
0.21 |
Amai Community |
0.30 |
Kiwoko |
0.17 |
4 |
Kisiizi |
0.21 |
Amudat |
0.45 |
Virika |
0.17 |
5 |
Buluba - Leprosy |
0.23 |
Anaka |
0.46 |
Kisiizi |
0.18 |
6 |
Rugarama |
0.24 |
Angal St. Luke |
0.54 |
Rushere Community |
0.19 |
7 |
St. Anthony's Tororo |
0.24 |
Apac |
0.64 |
Buluba - Leprosy |
0.19 |
8 |
St. Joseph Kitovu |
0.25 |
Atutur |
0.96 |
Villa Maria |
0.19 |
9 |
Abim |
0.25 |
Bududa |
0.62 |
Nkokonjeru |
0.23 |
10 |
Virika |
0.26 |
Bugiri |
0.64 |
St. Anthony's Tororo |
0.23 |
Table 9: Output of Tobit Regression
VRSDEAIneffScore |
Coef |
Std. Error |
t |
p>(t) |
[95% Conf. Interval] Lower Upper |
|
Ownership |
-.2373533 |
.1566698 |
-1.51 |
0.134 |
-.5498213 |
.0751147 |
Hospsize |
.3064594 |
.1306399 |
2.35 |
0.022** |
.0459062 |
.5670126 |
Propqualstaff |
-.0090919 |
.0050784 |
-1.79 |
0.078 |
-.0192205 |
.0010367 |
Geographical location |
.2581932 |
.1243699 |
2.08 |
0.042** |
.0101453 |
.5062411 |
BOR |
-.0016612 |
.0021884 |
-0.76 |
0.450 |
-.0060258 |
.0027035 |
TrainingStatus |
.2620071 |
.1297391 |
2.02 |
0.047** |
.0032505 |
.5207637 |
OPDIBD |
.0816522 |
.085905 |
0.95 |
0.345 |
-.0896799 |
.2529843 |
AvStayADM |
.1948153 |
.0580116 |
3.36 |
0.001** |
.0791149 |
.3105158 |
_cons |
.2592612 |
.4617965 |
0.56 |
0.576 |
-.6617628 |
1.180285 |
Var (e. VRSDEAIneffScore |
.2232797 |
.0383208 |
|
|
.1585589 |
.3144181 |
**Statistically significant at 5% level of significance