Is the 2015 eye care service delivery profile in Southeast Asia closer to universal eye health need!

Purpose: The year 2015 status of eye care service profile in Southeast Asia countries was compared with year 2010 data to determine the state of preparedness to achieve the World Health Organization global action plan 2019. Methods: Information was collected from the International Agency for Prevention of Blindness country chairs and from the recent PubMed referenced articles. The data included the following: blindness and low vision prevalence, national eye health policy, eye health expenses, presence of international non-governmental organizations, density of eye health personnel, and the cataract surgical rate and coverage. The last two key parameters were compared with year 2010 data. Results: Ten of 11 country chairs shared the information, and 28 PubMed referenced publications were assessed. The prevalence of blindness was lowest in Bhutan and highest in Timor-Leste. Cataract surgical rate was high in India and Sri Lanka. Cataract surgical coverage was high in Thailand and Sri Lanka. Despite increase in number of ophthalmologists in all countries (except Timor-Leste), the ratio of the population was adequate (1:100,000) only in 4 of 10 countries (Bhutan, India, Maldives and Thailand), but this did not benefit much due to unequal urban-rural divide. Conclusion: The midterm assessment suggests that all countries must design the current programs to effectively address both current and emerging causes of blindness. Capacity building and proportionate distribution of human resources for adequate rural reach along with poverty alleviation could be the keys to achieve the universal eye health by 2019. Keywords: Eye care delivery; Southeast Asia; Universal eye health

A cross-sectional survey of critical care services in Sri Lanka: a lower middle-income country

To describe the extent and variation of critical care services in Sri Lanka as a first step towards the development of a nationwide critical care unit (CCU) registry. A cross-sectional survey was conducted in all state CCUs by telephone or by visits to determine administration, infrastructure, equipment, staffing, and overall patient outcomes. There were 99 CCUs with 2.5 CCU beds per 100000 population and 13 CCU beds per 1 000 hospital beds. The median number of beds per CCU was 5. The overall admissions were 194 per 100000 population per year. The overall bed turnover was 76.5 per unit per year, with CCU mortality being 17%. Most CCUs were headed by an anesthetist. There were a total of 790 doctors (1.6 per bed), 1,989 nurses (3.9 per bed), and 626 health care assistants (1.2 per bed). Majority (87.9%) had 1:1 nurse-to-patient ratio, although few (11.4%) nurses had received formal intensive care unit training. All CCUs had basic infrastructure (electricity, running water, piped oxygen) and basic equipment (such as electronic monitoring and infusion pumps). Sri Lanka, a lower middle-income country has an extensive network of critical care facilities but with inequalities in its distribution and facilitie

Point Prevalence Survey of Antimicrobial Use in Selected Tertiary Care Hospitals of Pakistan Using WHO Methodology: Results and Inferences

Background and objectives: The inappropriate use of antibiotics in hospitals can potentially lead to the development and spread of antibiotic resistance, increased mortality, and high economic burden. The objective of the study was to assess current patterns of antibiotic use in leading hospitals of Pakistan. Moreover, the information collected can support in policy-making and hospital interventions aiming to improve antibiotic prescription and use. Methodology and materials: A point prevalence survey was carried out with data abstracted principally from patient medical records from 14 tertiary care hospitals. Data were collected through the standardized online tool KOBO application for smart phones and laptops. For data analysis, SPSS Software was used. The association of risk factors with antimicrobial use was calculated using inferential statistics. Results: Among the surveyed patients, the prevalence of antibiotic use was 75% on average in the selected hospitals. The most common classes of antibiotics prescribed were third-generation cephalosporin (38.5%). Furthermore, 59% of the patients were prescribed one while 32% of the patients were prescribed two antibiotics. Whereas the most common indication for antibiotic use was surgical prophylaxis (33%). There is no antimicrobial guideline or policy for 61.9% of antimicrobials in the respected hospitals. Conclusions: It was observed in the survey that there is an urgent need to review the excessive use of empiric antimicrobials and surgical prophylaxis. Programs should be initiated to address this issue, which includes developing antibiotic guidelines and formularies especially for empiric use as well as implementing antimicrobial stewardship activities

Applicability of the APACHE II model to a lower middle income country

Purpose: To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data. Materials and methods: Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE). Results: From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively. Conclusions: An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted. (c) 2017 Elsevier Inc. All rights reserve