Prevalence and factors associated with Nosocomial infections in Public Hospitals of Rwanda : A Cross Sectional Study.

doi:10.21203/rs.3.rs-4887480/v1

Background

Healthcare-associated infections are a major global public health problem. This study aims at establishing the prevalence and factors associated with nosocomial infections among patients admitted in public Hospitals of Rwanda.

Methods

Hospital-based cross-sectional study was conducted with a structured format to consider for presence of nosocomial infections among 733 participants. Patients were selected through systematic random technique. Multivariate logistic regression was computed to identify factors associated with nosocomial infections and variables with a p-value < 0.05 were considered statistically significant.

Results

National prevalence is 21.7%. The main factors positively associated with nosocomial infections ( risk factors ) were as follows: high temperature (AOR = 12.08, 95% CI: 5.69–25.65, p-value < 0.001); patients operated by a General Practitioner (AOR = 21.71, 95% CI, 1.67 -281.89,p-value = 0.02.) compared to those with surgeon; patients with postoperative hematocrit (HCT) levels exceeding 30% (AOR = 170.5, 95% CI: 1.77–16436.2, p-value = 0.03) compared to those with HTC levels ≤ 30%; patients who did not take prophylactic antibiotics (AOR = 8.61, 95% CI: 1.37–54.02, p-value = 0.02 )compared to those who took antibiotics; patients with long stay lasting four days or more (AOR = 190.36, 95% CI: 20.22–1791.86, p- value < 0.00) compared to those with shorter stay; patients with clean contaminated wounds (AOR = 6.8, 95% CI: 2.00–23.13, p-value < 0.001) and contaminated wounds (AOR = 3.66, 95% CI: 1.15–11.67, p-value = 0.03) compared to those to those with clean wounds ; patients operated for longer than one hour (AOR = 3.9, 95% CI: 1.33–11.43, p-value = 0.01) compared to those operated less than an hour. Besides, the following factors were negatively associated with nosocomial infections ( protective factors ); patients not immunosuppressed were less likely to develop nosocomial infections (AOR = 0.46, 95% CI [1.07–1.20], p-value = 0.01 ) compared to those with weak immune system ; patients with no Cesarean Section (C/S) history were less likely to develop nosocomial infections ( AOR = 0.11, 95% CI, 0.02–0.62, p-value 0.01) compared to those with C/S history ; patients without drain were less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.06–0.60, p-value = 0.01) compared to those with drain; patients operated in areas other than the abdomen were less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.04–0.81, p-value = 0.03) compared to those with abdomen as area of operation.

Conclusion and recommendations :

Briefly we recommend that all healthcare workers should strive for better strategies to minimize the prevalence of nosocomial infections, and this may be achieved by being vigilent to patients with higher temperature, improving IPC implementation (hand hygiene,complete PPE, availability of desinfectants, and other protective measures ) as recommeded by WHO, better diagnosis for reducing ALOS, giving antibiotic-prophylaxis to patients programmed for operations 24 hours before surgery.

Prevalence

Healthcare associated infection

Associated factors

Nosocomial infections

Healthcare Acquired Infections (HAI) are increasingly becoming a major global public health problem posing great threat to patient safety and wellbeing of healthcare providers. HAIs contribute to increased morbidity and mortality, excess health care cost and prolonged hospital stay [1, 2].

Epidemiological data support the position that HAI risk is associated with multiple variables such as patient’s age, pre-existing underlying disease, length of hospital stay, an immune-compromised state, and the presence of invasive medical devices during hospitalization such as urinary catheters, central lines, and ventilation) and also HAIs can be prevented through adequate preventive measures [3].

Surveillance has been accepted as a primary element of preventing and controlling HAIs. Although continuous prospective surveillance for HAIs is the gold standard, this approach requires comprehensive resources. Therefore, point prevalence surveys are preferred for determining the magnitude of HAIs in resource poor settings. Such studies are inexpensive and do not require extensive resources [3].

Healthcare associated infections (HAIs), occur worldwide and mainly affect low and middle income countries, which believed to be 5.7–19.1 times greater than that of developed world[4]. The overall estimate indicated that more than 2.5 million cases of HAI occur each year in the developing and developed world[5]. About 90% of the infections were occurring in a resource limited setting [6].

HAIs can be associated with antibiotic resistant pathogens which make treatment more difficult and expensive. In Europe about one-third of micro-organisms isolated in HAI are resistant to antibiotics[7]. In Africa, higher rates of antibiotic resistant between 10–100% are reported among isolates from HAIs [8, 9].

Hospital-acquired infections are a guideline containing a multitude of protocols needed to be implemented by HCWs, thus greatly reducing the magnitude of HAIs. Several factors such as poor awareness among HCWs and compliance associated with personal,logistical and organizational barriers exert their own effect on proper application of these protocol[10].

According to a report of the World Health Organization (WHO) on 55 hospitals in 14 countries, 8.7% of the hospitalized patients had HAIs, which were more prevalent in the Eastern Mediterranean Region and less prevalent in the West of the Pacific [11, 12].

The prevalence rate of these infections was reported to be 5% in the Nort of America and some parts of Europe, while in some Asian, Latino-America, and African countries it was about 40% [13, 14].

In Rwanda, few informations regarding the prevalence of nosocomial infections have been done in some referral and district hospitals (CHUK, CHUB, Gitwe,, Masaka) and the prevalence varied between 12–50% (intensive care CUHK). Hence this study was deemed necessary. The aim of this study conducted in Public Hospitals of Rwanda was to study the local epidemiology of HAIs and major related factors in the country.

This study used the criteria of the Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network ( NHSN) [15].

Study setting

The study was conducted in three departments of Obstetrics – Gynecology, Surgery and Internal Medicine in Public Hospitals of Rwanda scattered in four regions (Northern, Southern, Eastern, Western) and Kigali City. These hospitals are made up with Referral, Provincial and District hospitals and are serving the majority of Rwandan population.

Study design

This study was a cross-sectional study design among patients admitted in three departments (Obstetrics–Gynecology, Surgery and Internal Medicine) for a period of one year from 01/January to 31 December 2022.

Study populations and eligibility criteria

All patients who have been hospitalized during the year 2022 in the three departments of public hospitals with age from 1 month and above in Internal Medicine, Surgery while in Obstetrics –Gynecology from 15–35 years were considered as the study population. All patients admitted from 48 hours and 72 hours, readmission within 7 days after discharge in Internal Medicine, 30 days after surgical operation, 1 year after operation for prosthesis were eligible for the study. Meanwhile patients admitted for a period under 48 hours were excluded.

Sample size calculation and sampling techniques.

By assuming prevalence (p) = 50% since there was no previous similar study at the public hospitals, 4% margin of error at 96% confidence interval will be used. This is for increasing the sample size.

N =( Z² x P x Q) / D²

N = Desired sample size

Z = Standard normal deviated set at 2.05 which corresponds to 96% confidence level.

P = Proportion in the target population estimated to 50%.

Q = Proportion 0.5

D = Standard error assumed to be 4%

N = (2.05)² X 0.5 X 0.5 = 656.64 files so, 657 files will be examined.

(0.04)²

Patients underwent treatment within the selected units and hospitals for a period of 1 year with the mentioned symptoms will be considered as nosocomial infection.

For this study the required number of study participants was calculated by using single population proportion with standard error assumed to be 4%, it was 657, then adding 12% the final sample size was estimated to be 733 patients. A multistage systematic random sample technique was used to select the medical records of patients who were hospitalized in 3 departments .In Obstetrics-Gynecology, the record was focusing on mothers who underwent Cesarean Section (C/S) only in referral, provincial and district hospitals.Number of medical records of patients to be reviewed was allocated proportionally for each selected hospital, then finally a systematic random sampling technique using medical registration number as a sampling frame with K value of 5 ( k = 5) by selecting the first one using lottery method was used to recruit medical records of patients for each selected hospital.

Data collection tools and procedures

Data were extracted from the selected medical records using a structured format that was developed by the principal investigator based on the criteria of CDC/NHSN guideline[15]. The data collection was made by two trained nurses ,one resident working in the hospital. Supervision was made by the principal investigator. Selected socio-demographic, medical history related variables were extracted from the medical records of patients ( books, open-clinic, Open- EMRS ) .Nosocomial infection was defined based on CDC guideline [15]

Data management and analysis

The data were entered into SPSS version 20 software by the principal investigator. Descriptive data analysis was made using frequency and percentage. Bivariate and multivariate binary logistic regression analysis were done to identify the predictors of Nosocomial Infections. The outputs of the analysis are provided using crude (COR) and adjusted odds ratio (AOR). Variables that showed statistically significant association (p < 0.05) in the bivariate analysis were exported to the multivariate model.

1.Socio-demographic characteristics of the study population

As detailed in Table 1, a total of 733 patients were enlisted, the findings show a broad distribution across different areas as follows: District Hospitals 54.6%, Referral hospitals 23.7%, Provincial hospitals 21.7%. In terms of Provinces, Northern Province represented 20.5%, Eastern and Southern Provinces 20.2%, Western Province 20.1% and Kigali city 19.1%. In terms of gender equity females dominated with 69.7% while males 30.3%. In terms of age distribution, the bulk of participants was 36 years or older 47.1%, followed by those aged 19–35 years 36.2%, and 16.8% were 18 years old.

Table 1

Socio-demographic characteristics of the respondents
Characteristcs	Frequency ( N = 733 )	Percent ( % )
Type of Hospital
Referral	174	23.7
Provincial	159	21.7
District	400	54.6
Region
Kigali City	140	19.1
Southern Province	148	20.2
Northern Province	150	20.5
Eastern Province	148	20.2
Western Province	147	20.1
Departments
Internal Medicine	270	38.8
Obstetrics- Gynecology	264	36
Surgery	199	27.1
Gender
Male	222	30.3
Female	511	69.7
Age( in years)
0–18	123	16.8
19–35	265	36.2
> = 36	345	47.1

2.Prevalence of Nosocomial infections

The findings reveal variations in the prevalence of nosocomial infections across different departments, with Internal Medicine reporting the highest prevalence at 22.6%, followed by Surgery at 21.6% and Obstetrics Gynecology at 20.8%. The average percentage in Rwanda is 21.7%, as shown in Fig. 1.

3. Factors associated with nosocomial infections.

3.1.Factors associated with nosocomial infections in Internal Medicine

As shown in Table 2, patients with high temperature were 12 times more likely to develop nosocomial infections compared to those with low temperature (AOR = 12.08, 95% CI: 5.69–25.65, p-value < 0.001). Furthermore, patients who were not immunosuppressed were 0.46 times less likely to develop nosocomial infections (AOR = 0.46, 95% CI [1.07–1.20], p-value = 0.01) compared to those immunosuppressed.

Table 2

Factors associated with nosocomial infections in Internal Medicine
Variables	Descriptive			Bivariate		Multivariate
Variables	Total N (%)	Yes N(%)	No N(%)	COR [95% CI]	p-value	AOR [95% CI]	p-value
Type of Hospital
Referral	75 (27.8)	55 (73.3)	20 (26.7)	1		1
Provincial	60 (22.2)	49 (81.7)	11 (18.3)	0.62 [0.27–1.41]	0.26	0.82 [0.30 2.28]	0.71
District	135 (50)	105 (77.8)	30 (22.2)	0.79 [0.41 1.51]	0.47	1.19 [0.53 2.69]	0.68
Region
Kigali city	30 (11.1)	24 (80.0)	6 (20.0)	1		1
Southern	60 (22.2)	44 (73.3)	16 (26.7)	1.45 [0.50 4.20]	0.49	0.73 [0.20 2.75]	0.65
Northern	60 (22.2)	49 (81.7)	11 (18.3)	0.9 [0.29 2.72]	0.85	0.79 [0.22 2.89]	0.73
Eastern	60 (22.2)	49 (81.7)	11 (18.3)	0.9 [0.29 2.72]	0.85	0.55 [0.15 2.06]	0.37
Western	60 (22.2)	43 (71.7)	17 (28.3)	1.58 [0.58 4.55]	0.4	1.21 [0.35 4.18]	0.77
Age ( in years )
18 years	26 (9.6)	23 (88.5)	3 (11.5)	1		1
30 years	86 (31.9)	73 (84.9)	13 (15.1)	1.37 [0.36 5.21]	0.65	1.43 [0.33 6.23]	0.64
> = 31	158 (58.5)	113 (71.5)	45 (28.5)	3.05 [0.87–10.67]	0.08	2.55 [0.63–10.33]	0.19
Gender
Male	130(48.1)	97 (74.6)	33 (25.4)	1		1
Female	140 (51.9)	112 (80.0)	28 (20.0)	0.73 [0.41 1.30]	0.29	0.92 [0.46 1.85]	0.81
Temperature
Low (< 37)	194 (71.8)	20 (10.3)	174 (89.7)	1		1
High (> 37)	76 (28.2)	41 (53.9)	35 (46.1)	10.19 [5.3419.45]	< 0.001	12.08 [5.69–25.65]	< 0.001
Immunosupressed
Yes	44 (16.3)	26 (59.1)	18 (40.9)	1		1
No	226 (83.7)	183 (81.0)	43 (19.0)	0.34 [0.17 0.67]	< 0.00	0.46 [1.07 1.20]	0.01
Diabetes
Yes	35 (13)	24 (68.6)	11 (31.4)	1		1
No	235 (87)	185 (78.7)	50 (21.3)	0.59 [0.27 1.29]	0.18	1.28 [0.41 4.02]	0.67

3.2. Factors associated with nosocomial infections in Obstetrics-Gynecology

As shown in Table 3, patients who had no Cesarean Section (C/S) history, were 0.11 times less likely to develop nosocomial infections ( AOR = 0.11, 95% CI, 0.02–0.62, p = 0.01) compared to those with C/S history. Patients who underwent C/S with GP, were 21.71 times more likely to develop nosocomial infections (AOR = 21.71, 95% CI, 1.67 -281.89,p-value = 0.02.) compared to those with surgeon. Morever patients with postoperative hematocrit (HCT) levels exceeding 30% were 170.5 times more likely to develop nosocomial infections (AOR = 170.5, 95% CI: 1.77–16436.2, p-value = 0.03) compared to those with HCT levels ≤ 30%. Patients who did not take prophylactic antibiotics were 8.6 times more likely to develop nosocomial infections (AOR = 8.61, 95% CI: 1.37–54.02, p-value = 0.02 ) compared to those with prophylactic antibiotics. Patients with hospital stay (ALOS ) lasting four days or more were 190.3 times more likely to develop nosocomial infections (AOR = 190.36, 95% CI: 20.22–1791.86, p- value < 0.00) compared to those with shorter stay.

Table 3

Factors associated with nosocomial infections in Obstetrics Gynecology
Variables	Descriptive			Bivariate analysis		Multivariate analysis
Variables	Total N(%)	Yes N(%)	No N(%)	COR [95% CI]	p-value	AOR [95% CI]	p-value
Type of Hospital
Referral	44 (16.7)	34 (77.3)	10 (22.7)	1		1
Provincial	55 (20.8)	41 (74.5)	14 (25.5)	1.16 [0.46–2.94]	0.75	0.45 [0.05–4.24]	0.49
District	165 (62.5)	134(81.2)	31 (18.8)	0.79 [0.351.76]	0.56	0.36 [0.05–2.62]	0.32
Region
Kigali city	88 (33.3)	69 (78.4)	19 (21.6)	1		1
Southern Province	44 (16.7)	35 (79.5)	9 (20.5)	0.93 [0.38–2.28]	0.88	2.25 [0.24–20.77]	0.47
Northern Province	44 (16.7)	33 (75.0)	11 (25.0)	1.21 [0.52–2.83]	0.66	7.84 [0.75 81.92]	0.09
Eastern Province	44 (16.7)	36 (81.8)	8 (18.2)	0.81 [0.32–2.02]	0.65	0.21 [0.02–2.86]	0.24
Western Province	44 (16.7)	36 (81.1)	8 (18.2)	0.81 [0.32–2.02]	0.65	2.82 [0.31–25.89]	0.36
Age (in years)
17–25	70 (26.5)	51 (72.9)	19 (27.1)	1		1
26–30	100 (37.9)	83 (83.0)	17 (17.0)	0.55 [0.26–1.15]	0.11	2.54 [0.27–23.70]	0.41
> = 31	94 (35.6)	75 (79.8)	19 (20.2)	0.68 [0.33–1.41]	0.3	2.7 [0.34–21.14]	0.35
Ante Partum Hemorrage
Yes	28 (10.6)	20 (71.4)	8 (28.6)	1		1
No	236 (89.4)	189 (80.1)	47 (19.9)	0.62 [0.26–1.50]	0.29	0.56 [0.04–8.09]	0.67
Post Partum Hemorrage
Yes	29 (11)	27 (91.3)	2 (6.9)	1		1
No	235 (89)	182 (77.4)	53 (22.6)	3.93 [0.91–17.07]	0.07	6.58 [0.38–115.20]	0.2
Parity
0–1	125 (47.3)	98 (78.4)	27 (21.6)	1		1
02–4	109 (41.3)	89 (81.7)	20 (18.3)	0.82 [0.43–1.56]	0.54	0.33 [0.04–2.87]	0.32
>=5	30 (11.4)	22 (73.3)	8 (26.7)	1.32 [0.53–3.29]	0.55	0.39 [0.02–9.28]	0.56
ANC Visit
Yes	224 (84.8)	174 (77.7)	50 (22.3)	1		1
No	40 (15.2)	35 (87.5)	5 (12.5)	0.5 [0.19–1.34]	0.17	0.87 [0.12–6.34]	0.89
Gestational age (in weeks)
< 37	15 (5.7)	13 (86.7)	2 (13.3)	1		1
37–40	232 (87.9)	190 (81.9)	42 (18.1)	1.44 [0.31–6.61]	0.64	0.81 [0.04–14.87]	0.89
> 40	17 (6.4)	6 (35.3)	11 (64.7)	11.9 [1.99–71.41]	0.01	10.37 [0.215–11.30]	0.24
Number of vaginal examinations
Never done	47 (17.8)	39 (83.0)	8 (17.0)	1		1
1–4 times	183 (69.3)	149(81.4)	34 (18.6)	1.11 [0.48–2.59]	0.81	0.85 [0.08–8.91]	0.89
>= 5times	34 (12.9)	21 (61.8)	13 (38.2)	3.02 [1.08–8.44]	0.04	4.73 [0.31–72.40]	0.26
Duration of membrane rupture
Intact	106 (40.2)	88 (83.0)	18 (17.0)	1		1
Ruptured < 12hrs	135 (51.1)	105(77.8)	30 (22.2)	1.4 [0.73–2.67]	0.31	1.69 [0.41–6.99]	0.47
Ruptured > = 12hrs	23 (8.7)	16 (69.6)	7 (30.4)	2.14 [0.77–5.95]	0.15	8.45 [0.77–92.55]	0.08
History of Cesarean Section C/S
Yes	70 (26.5)	51 (72.9)	19 (27.1)	1		1
No	194 (73.5)	158 (81.4)	36 (18.6)	0.61 [0.13–1.16]	0.13	0.11 [0.02–0.62]	0.01
Presence of meconium
Yes	64 (24.2)	49 (76.6)	15 (23.4)	1		1
No	200 (75.8)	160 (80.0)	40 (20.0)	0.82 [0.42–1.60]	0.56	1.25 [0.24–6.57]	0.79
Chorioaminiotis
Yes	12 (4.5)	11 (91.7)	1 (8.3)	1		1
No	252 (95.5)	198 (78.6)	54 (21.4)	3 [0.38–23.75]	0.3	20.42 [0.64–646.90]	0.09
Responsible person Who has done operation
Surgeon	46 (17.4)	41 (89.1)	5 (10.9)	1		1
Gynecologist	98 (37.1)	71 (72.4)	27 (27.6)	3.12 [1.11–8.72]	0.03	7.00 [0.61–79.97]	0.12
General Practitioners (GP)	100 (37.9)	77 (77.0)	23 (23.0)	2.45 [0.87–6.92]	0.09	21.71 [1.67–28.89]	0.02
Intern	20 (7.6)	20 (100.0)	0 (0.0)	1		1
Preoperative HCT
<= 30%	45 (17)	26 (57.8)	19 (42.2)	1		1
> 30%	219 (83)	183 (83.6)	36 (16.4)	0.27 [0.13–0.54]	< 0.00	2.72 [0.21–34.90]	0.44
Postoperative HTC
<= 30%	13 (4.9)	11 (84.6)	2 (15.4)	1		1
> 30%	251 (95.1)	198 (78.9)	53 (21.1)	0.27 [0.13–0.54]	< 0.00	170.51 [1.77-16436.21]	0.03
Type of Anesthesia
Regional	243 (92)	199 (81.9)	44 (18.1)	1		1
General	21 (8)	10 (47.6)	11 (52.4)	4.98 [1.99–12.44]	< 0.00	17.36 [1.00-302.23]	0.05
Operation duration
< 1 hr	210 (79.5)	176 (83.3)	34 (16.2)	1		1
>=1 hr	54 (20.5)	33 (61.1)	21 (38.9)	3.29 [1.70–6.37]	< 0.00	5.81 [0.81–41.72]	0.08
Type of Incision
Transversal	243 (92)	192 (79.0)	51 (21.0)	1		1
Vertical	21 (8)	17 (81.0)	4 (19.0)	0.89 [0.29–2.75]	0.83	0.28 [0.03–2.69]	0.27
Prophylactic antibiotics
Yes	196 (74.2)	169 (86.2)	27 (13.8)	1		1
No	68 (25.8)	40 (58.8)	28 (41.2)	4.38 [2.33–8.24]	< 0.00	8.61 [1.37–54.02]	0.02
Average length of stay ( ALOS )
1–3 days	167 (63.3)	161 (96.4)	6 (3.6)	1		1
>= 4days	97 (36.7)	48 (49.5)	49 (50.5)	27.4 [11.06–67.84]	< 0.00	190.36 [20.22-1791.86]	< 0.00
Receiving Blood Transfusion
Yes	51 (19.3)	34 (66.7)	17 (33.3)	1		1
No	213 (80.7)	175 (82.2)	38 (17.8)	0.43 [0.22–0.86]	< 0.02	0.27 [0.03–2.19]	0.22
Gestity (Mean ± SD)	-	3.07 ± 1.28	2.98 ± 1,37	0.95 [0.75–1.20]	0.65	1.21 [0.59–2.47]	0.61

3.3. Factors associated with nosocomial infections in Surgery

As shown in Table 4, female patients were 0.35 times less likely to develop nosocomial infections (AOR = 0.35, 95% CI: 0.13–0.95, p-value = 0.04) compared to their male counterparts. Additionally, patients with clean contaminated wounds were 6.8 times more likely to develop nosocomial infections (AOR = 6.8, 95% CI: 2.00–23.13, p-value < 0.001) compared to those with clean wounds, as well as patients with contaminated wounds were 3.6 times more likely to develop nosocomial infections (AOR = 3.66, 95% CI: 1.15–11.67, p-value = 0.03) compared to those with clean wounds. Patients without drain were 0.19 less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.06–0.60, p-value = 0.01) compared to those with drain. Patients operated for longer than one hour were 4 times more likely to develop nosocomial infections (AOR = 3.9, 95% CI: 1.33–11.43, p-value = 0.01) compared to those with < 1 hour. Moreover, patients who underwent surgeries in areas other than the abdomen were 0.19 times less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.04–0.81, p-value = 0.03) compared to those with abdomen as area of operation.

Table 4

Factors associated with nosocomial infections in Surgery
Variables		Descriptive			Bivariate analysis		Multivariate analysis
Variables		Total N(%)	Yes N(%)	No N(%)	COR [95% CI]	p-value	AOR [95% CI]	p-value
Type of Hospital
Referral		55 (27.6)	42 (76.36)	13 (23.64)	1		1
Provincial		44 (22.1)	34 (77.27)	10 (22.73)	0.95 [0.37 2.43]	0.92	0.46 [0.13–1.70]	0.24
District		100 (50.3)	80 (80.00)	20 (20.00)	0.81 [0.37 1.78]	0.6	0.53 [0.18–1.53]	0.24
Region
Kigali City		22 (11.1)	17 (77.27)	5 (22.73)	1		1
Southern Province		44 (22.1)	35 (79.55)	9 (20.45)	0.87 [0.25 3.01]	0.83	0.86 [0.16–4.76]	0.87
Northern Province		46 (23.1)	34 (73.91)	12 (26.09)	1.2 [0.36 3.96]	0.77	2.17 [0.4011.85]	0.37
Eastern Province		44 (22.1)	36 (81.82)	8 (18.18)	0.76 [0.21 2.66]	0.66	1.32 [0.23–7.63]	0.76
Western Province		43 (21.6)	34 (79.07)	9 (20.93)	0.9 [0.26 3.11]	0.87	1.52 [0.27–8.63]	0.64
Ages ( in years )
1–18		27 (13.6)	26 (96.29)	1 (3.70)	1		1
19–35		79 (39.7)	60 (75.95)	19 (24.05)	8.23 [1.0564.78]	0.05	8.34 [0.78–89.09]	0.08
> = 36		93 (46.7)	70 (75.27)	23 (24.73)	8.54 [1.1066.50]	0.04	10.00 [0.95105.03]	0.06
Gender
Male		92 (46.2)	72 (78.26)	20 (21.73)	1		1
Female		107 (53.8)	84 (78.50)	23 (21.49)	0.99 [0.50 1.94]	0.97	0.35 [0.13–0.95]	0.04
Wound Classification
Clean	103 (51.8)		91 (88.34)	12 (11.65)	1		1
Clean contaminated	39 (19.6)		22 (56.41)	17 (43.58)	5.86 [2.4514.04]	0	6.8 [2.0023.13]	0.00
Contaminated	39 (19.6)		28 (28.20)	11 (28.21)	2.98 [1.19 7.49]	0.02	3.66 [1.1511.67]	0.03
Dirty	18 (9)		15 (83.33)	3 (16.67)	1.52 [0.38 6.02]	0.55	2.15 [0.4510.38]	0.34
Immunosuppressed patient
Yes		17 (8.5)	16 (94.11)	1 (5.88)	1		1
No		182 (91.5)	140(79.92)	42 (23.07)	4.8 [0.6237.27]	0.13	4.6 [0.4151.83]	0.22
Smoking
Yes		26 (13.1)	20 (76.92)	6 (23.07)	1		1
No		173 (86.9)	136(78.61)	37 (21.38)	0.91 [0.34 2.42]	0.85	0.64 [0.18–2.28]	0.5
Diabetes
Yes		27 (13.6)	24 (88.88)	3 (11.11)	1		1
No		172 (86.4)	132(76.74)	40 (23.25)	2.42 [0.69 8.47]	0.17	1.99 [0.47–8.39]	0.35
Hemoglobin %
< 10 mg/dl		35 (17.6)	20 (57.14)	15 (42.85)	1		1
> 10 mg/dl		164 (82.4)	136(82.92)	28 (17.07)	0.27 [0.13 0.60]	< 0.00	0.48 [0.13–1.72]	0.26
Drain
Yes		36 (18.1)	21 (58.33)	15 (41.67)	1		1
No		163 (81.9)	135(82.82)	28 (17.17)	0.29 [0.13–0.63]	< 0.00	0.19 [0.06–0.60]	0.01
Surgery
Emergency		75 (37.7)	58 (77.33)	17 (22.67)	1		1
Planned		124 (62.3)	98 (79.03)	26 (20.96)	0.91 [0.45 1.81]	0.78	1.4 [0.52–3.75]	0.5
Duration of operation
< 1 hour		162 (81.4)	135(83.33)	27 (16.67)	1		1
> 1 hour		37 (18.6)	21 (56.76)	16 (43.24)	3.81 [1.76 8.23]	< 0.00	3.9 [1.3311.43]	0.01
Area of operation
Abdomen		30 (15.1)	17 (56.67)	13 (43.33)	1		1
Other part		169 (84.9)	139(82.24)	30 (17.75)	0.28 [0.12 0.64]	< 0.00	0.19 [0.04–0.81]	0.03
Received blood transfusion
Yes		43 (21.6)	29 (67.4)	14 (32.6)	1		1
No		156 (78.4)	127 (81.4)	29 (18.6)	0.47 [0.22 1.01]	0.05	1.89 [0.37–9.69]	0.45

This study aimed at determining the national prevalence and factors associated with nosocomial infections (NIS) or HAIs in Public Hospitals of Rwanda. It has been done in three departments (Internal Medicine, Surgery and Obstetrics-Gynecology ).733 patients were enrolled in this study. The study showed that the national average prevalence of nosocomial infections was 21.7% and Internal Medicine was predominant with 22.6% .

According to a report of the World Health Organization (WHO) on 55 hospitals in 14 countries, it has been seen that in Eastern Mediterranean Region and West of Pacific the prevalence of nosocomial infections for hospitalized patients was 8.7% [12, 16]. In North America and some parts of Europe, the prevalence was reported to be only 5%, while African countries, some Asian and Latino-America countries the prevalence of nosocomial infections was about 40% [16].

Based on these findings, as the point prevalence of nosocomial infections in Public Hospitals of Rwanda is 21.7%, it seems to be high. Although a number of studies have been conducted on different parts of our country to determine the prevalence rate of HAIs, many of them have been conducted in one to three hospitals only.

In 2016, Kalibushi et al., have done a study on prevalence and risk factors for post cesarean delivery at CHUB, and findings were 4.9% [17].

In 2016, Stephanie Lukas et al., studied on establishment of a hospital-acquired infection surveillance at CHUK, a baseline study in different departments of the hospital and findings revealed that the prevalence of HAIs were dominant in ICU and Private rooms 50%, Internal Medicine 7.4% and Maternity 2.9% and General Surgery 12.1% [18].

In 2018, Nkurunziza et al. studied the prevalence and predictors of surgical – site infection after cesarean section in a rural hospital in Rwanda, the prevalence was 10.9% [19] ,these findings seem to be high in one hospital, compared to the natonal prevalence 20.8% after cesarean section. So, many studies in Rwanda varied between 10–50%.

Generally, findings suggest that nosocomial infections are widespread in sub Saharan Africa with surgical sites being the most common. So, the prevalence of nosocomial infections have been reported to vary between 1.6%- 28.7%. Ghana 9.6%, Mali 18.7%, DRC 1.7% Burundi 10.4%, Uganda 28% [20]. Compared to our findings, we see that Rwandan prevalence of 20.8 is high. In Nigeria and Ethiopia, the total accruing occurrence in surgical wards has been reported to vary from 5.7%- 45.8% [20].

This study showed that the main associated factors with nosocomial infections were as follows :

Patients with higher temperature,were 12 times more likely to develop nosocomial infections compared to those with low temperature (AOR = 12.08, 95% CI: 5.69–25.65, p-value < 0.001). The higher temperarture is showing infection in human or animal body, so patients admitted for a period of 48 hours and over with the increase of temperature ( 38–40 ⁰C) is considered as NI[15].

Patients who were not immunosuppressed were 0.46 times less likely to develop nosocomial infections (AOR = 0.46, 95% CI [1.07–1.20], p-value = 0.01) compared to those with weak immune system. This is obvious medically as the status of being immunosuppressed is a predisposing factor for the bacteria, fungi, viruses to invase easily the human body. This study is supported by an other study done in China showing the risk factors for nosocomial infection in RICU(Respiratory Intensive Care Unit). So, in the underlying diseases (lung cancer), trauma, diabetes mellitus, immunosuppressive therapy, endotracheal intubation, tracheotomy, utilization of urinary catheter, central catheter and ventilator were identified as risk factors [21].

Patients who had no Cesarean Section (C/S) history, were 0.11 times less likely to develop nosocomial infections (AOR = 0.11, 95% CI, 0.02–0.62, p = 0.01) compared to those with C/S history, this may be due to previous scar on body being a predisposing factor to nosocomial infections [22].

Patients who underwent C/S with General Practitioner (GP), were 21.71 times more likely to develop nosocomial infections (AOR = 21.71, 95% CI, 1.67 -281.89,p-value = 0.02.) compared to those with surgeon. This is obvious, because a surgeon is more experienced to operation than a general practitioner (GP).

Patients with postoperative hematocrit (HCT) levels exceeding 30% were 170.5 times more likely to develop nosocomial infections (AOR = 170.5, 95% CI: 1.77–16436.2, p-value = 0.03) compared to those with HCT levels ≤ 30%. In Obstetrics-Gynecology, this study is similar to the study done in USA which finding demonstrated that higher postoperative hematocrit levels were associated with higher weight [23]. This is obvious because obesity is predisposing factor for nosocomial infections.

Patients who did not take prophylactic antibiotics were 8.6 times more likely to develop nosocomial infections (AOR = 8.61, 95% CI: 1.37–54.02, p-value = 0.02 ) compared to those with prophylactic antibiotics .This has been demonstrated that antibiotic prophylaxy was better to prevent nosocomial infections [24]. It is important to highlight that the antibiotic prophylaxis prior to surgery continues to be a much-discussed topic and as we know, is part of one of the operative stages. So, it is suggested to use cefazolin 1 gr or azithromycin 500 mg before the cesarean section [25]. This study is similar to the study done in Iran[26].

Patients with hospital stay (ALOS ) lasting four days or more were 190.3 times more likely to develop nosocomial infections (AOR = 190.36, 95% CI: 20.22–1791.86, p- value < 0.00) compared to those with shorter stay. This is because health facilities in their environment is the host of many bacteria, virus, so delaying in that environment can expose to the nosocomial infections.This study is similar to the study done in Palestine saying that Extending the ALOS by one day has been linked to the likelihood of raising the potential of acquiring an infection by 1.37%[27].

Female patients were 0.35 times less likely to develop nosocomial infections (AOR = 0.35, 95% CI: 0.13–0.95, p-value = 0.04) compared to their male counterparts. Our study was different to a study done in Irak studying the relationship between Nosocomial Infection and gender. The highest percentage of infection was recorded in females who 51.8% compared to males who 31.4%, and in the case of urinary tract infections, the highest percentage of infection in females was 57.9% compared to males with 42.1% [28].

Patients with clean contaminated wounds were 6.8 times more likely to develop nosocomial infections (AOR = 6.8, 95% CI: 2.00–23.13, p-value < 0.001) compared to those with clean wounds. This may be done by the hands of healthcare providers during treatment by not changing gloves. This study is supported by the study done in Ghana showing the environmental contamination in relation to surgical wound infection, the bacteria types were more frequently isolated on the ward than from the theatre [29].

Patients with contaminated wounds were 3.6 times more likely to develop nosocomial infections (AOR = 3.66, 95% CI: 1.15–11.67, p-value = 0.03) compared to those with clean wounds. This may be done by the hands of healthcare providers during treatment by not changing gloves [29].

Patients without drain were 0.19 less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.06–0.60, p-value = 0.01) compared to those with drain. This result is supported by an other study done in Switzland saying that the general use of drains is discouraged. However, drains may be beneficial in specific surgical procedures [30].

Patients operated for longer than one hour were 4 times more likely to develop nosocomial infections (AOR = 3.9, 95% CI: 1.33–11.43, p-value = 0.01) compared to those with < 1 hour. As, the tiredness of healthcare providers, over one hour, the control of IPC measures decreases. This may be due to the prolongation of surgery duration[24].

Moreover, patients who underwent surgeries in areas other than the abdomen were 0.19 times less likely to develop nosocomial infections (AOR = 0.19, 95% CI: 0.04–0.81, p-value = 0.03) compared to those with abdomen as area of operation. This may be supported by the study done in 2014 by CIAOW study (Complicated Intra-Abdominal Infections Worldwide ). The study described the epidemiological, clinical, and treatment profiles of complicated intra-abdominal infections in a worldwide context. The overall mortality rate was 10.5%. Analyzing the subgroups of patients with severe sepsis and septic shock at hospital admission the mortality rate reached 36.5% [31].

The national prevalence of nosocomial infections in Public Hospitals of Rwanda is 21.7% and Internal medicine dominated with 22.6%, surgery department 21.6% and Obstetrics-Gynecology 20.8%.

For all patients after 48 hours of admission with increase temperature 38⁰C – 39⁰C, healthcare providers should think about nosocomial infections and continue to monitor it up to the discharge.

Patients immunosuppressed should be put in isolated wards, for special care like given prophylactic antibiotic, emphasize proper cleanings and minimize visitors.

For patients operated by GP rather than surgeon, GPs performing operations should have enough training of IPC.

For patients with HTC level > 30% in post operative, as HTC level exceeding 30% is related to obesity, healthcare providers should be vigilent with those patients by checking obesity.

For patients who do not take antibiotic prophylaxis, it’s better to prescribe antibiotics to all patients programmed for operation 24 hours before operation.

All patients should be aware about IPC measures, availability of water and soap, avoiding unnecessary movements, while healthcare providers should comply with the IPC measures implementation, by washing hands before and after each patient care, changing gloves for each patient, all these for avoiding cross-contaminations.

Drains should be done at necessary need only otherwise should be avoided.

Briefly we recommend that all healthcare workers should strive for better strategies to minimize the prevalence of nosocomial infections, and this may be achieved by being vigilent to patients with higher temperature, improving IPC implementation(hand hygiene,complete PPE, availability of desinfectants, and other protective measures) as recommeded by WHO, better diagnosis for reducing ALOS, giving antibiotic-prophylaxis to patients programmed for operations 24 hours before surgery.

ALOS

Average length of stay

ANC

Antenatal care

AOR

Adjusted Odds ratio

C/S

Cesarean Section

CDC

Centers for Disease Control and Prevention

CHUB

Centre Hospitalier Universitaire de Butare

CHUK

Centre Hospitalier Universitaire de Kigali

CI

Confidence Interval

CIAOW

Complicated Intra-Abdominal Infections Worldwide

COR

Crude Odds ratio

DRC

Democratic Republic of Congo

GP

General Practitioner

HAI

Health Acquired Infection

HCT

Hematocrit

ICU

Intensive Care Unity

IPC

Infection Prevention and Control

mg/dl

milligrams per deciliter

NHSN

National Healthcare Safety Network

NI

Nosocomial Infections

^OC

Celsius Degree

PPE

Personal Protective Equipment

USA

United State of America

WHO

World Health Organization

Ethical approval and consent to participate

Ethical clearance No 344/CMHS IRB/2023 was approved by the Institutional Review Board (IRB) of the University of Rwanda, College of Medicine and Health Sciences. Permission was obtained from the MOH . All information from participants was kept confidential by using code numbers and there was no personal identification which was left on the questionnaire.

Consent for publication

Not applicable

Availability of data and materials

The datasets used for the present study can be obtained from the corresponding author ([email protected]) upon reasonable request.

Competing interests

Authors report no conflicts of interest regarding the present study.

Funding

There is no funding for this study.

Author contributions

BJP wrote the proposal, collected data, analyzed the data, and drafted the paper. HJD analyzed and discussed the data and revised the manuscript. ER, TN, and CM approved the proposal with some revisions, analysis, and manuscript writing. All authors read and approved the final manuscript.

Acknowledgments

We express our gratitude to the DGs of Public Hospitals in Rwanda for facilitating data collection.

Authors' information (optional)

¹School of Public Health,College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda.

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No competing interests reported.

Prevalence and factors associated with Nosocomial infections in Public Hospitals of Rwanda : A Cross Sectional Study.

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion and recommendations :

Figures

Background

Materials and methods

Study setting

Study design

Study populations and eligibility criteria

Data collection tools and procedures

Data management and analysis

Results

1.Socio-demographic characteristics of the study population

2.Prevalence of Nosocomial infections

3.1.Factors associated with nosocomial infections in Internal Medicine

3.2. Factors associated with nosocomial infections in Obstetrics-Gynecology

3.3. Factors associated with nosocomial infections in Surgery

Discussion

Conclusion and Recommendations

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1