Bacterial Aetiology of Neonatal Sepsis and Antimicrobial Resistance Pattern at the Regional Referral Hospital, Dar es Salam, Tanzania; A Call to Strengthening Antibiotic Stewardship Program.
Mtebe Venance MajigoJackline MakupaZivonishe MwazyungaAnna LuogaJulius KisingaBertha MwamkoaSukyung KimAgricola JoachimPublished in: Antibiotics (Basel, Switzerland) (2023)
The diagnosis of neonatal sepsis in lower-income countries is mainly based on clinical presentation. The practice necessitates empirical treatment with limited aetiology and antibiotic susceptibility profile knowledge, prompting the emergence and spread of antimicrobial resistance. We conducted a cross-sectional study to determine the aetiology of neonatal sepsis and antimicrobial resistance patterns. We recruited 658 neonates admitted to the neonatal ward with signs and symptoms of sepsis and performed 639 automated blood cultures and antimicrobial susceptibility testing. Around 72% of the samples were culture positive; Gram-positive bacteria were predominantly isolated, contributing to 81%. Coagulase-negative Staphylococci were the most isolates, followed by Streptococcus agalactiae . Overall, antibiotic resistance among Gram-positive pathogens ranged from 23% (Chloramphenicol) to 93% (Penicillin) and from 24.7% (amikacin) to 91% (ampicillin) for Gram-negative bacteria. Moreover, about 69% of Gram-positive and 75% of Gram-negative bacteria were multidrug-resistant (MDR). We observed about 70% overall proportion of MDR strains, non-significantly more in Gram-negative than Gram-positive pathogens ( p = 0.334). In conclusion, the pathogen causing neonatal sepsis in our setting exhibited a high resistance rate to commonly used antibiotics. The high rate of MDR pathogens calls for strengthening antibiotic stewardship programs.
Keyphrases
- gram negative
- antimicrobial resistance
- multidrug resistant
- drug resistant
- acinetobacter baumannii
- septic shock
- acute kidney injury
- intensive care unit
- klebsiella pneumoniae
- healthcare
- primary care
- physical activity
- public health
- escherichia coli
- acute care
- high throughput
- quality improvement
- mental health
- cystic fibrosis
- emergency department
- low birth weight
- machine learning