TRANSPORTATION BARRIERS TO ACCESS HEALTH CARE FOR SURGICAL CONDITIONS IN MALAWI a cross sectional nationwide household survey

Abstract

Background It is estimated that nearly five billion people worldwide do not have access to safe surgery. This access gap disproportionately affects low-and middle-income countries (LMICs). One of the barriers to healthcare in LMICs is access to transport to a healthcare facility. Both availability and affordability of transport can be issues delaying access to health care. This study aimed to describe the main transportation factors affecting access and delay in reaching a facility for health care in Malawi. Methods This was a multi-stage, clustered, probability sampling with systematic sampling of households for transportation access to general health and surgical care. Malawi has an estimated population of nearly 18 million people, with a total of 48,233 registered settlements spread over 28 administrative districts. 55 settlements per district were randomly selected for data collection, and 2–4 households were selected, depending on the size. Two persons per household were interviewed. The Surgeons Overseas Assessment of Surgical need (SOSAS) tool was used by trained personnel to collect data during the months of July and August 2016. Analysis of data from 1479 households and 2958 interviewees was by univariate and multivariate methods. Results Analysis showed that 90.1% were rural inhabitants, and 40% were farmers. No formal employment was reported for 24.9% persons. Animal drawn carts prevailed as the most common mode of transport from home to the primary health facility - normally a health centre. Travel to secondary and tertiary level health facilities was mostly by public transport, 31.5 and 43.4% respectively. Median travel time from home to a health centre was 1 h, and 2.5 h to a central hospital. Thirty nine percent of male and 59% of female head of households reported lack financial resources to go to a hospital. Conclusion In Malawi, lack of suitable transport, finances and prolonged travel time to a health care centre, all pose barriers to timely access of health care. Improving the availability of transport between rural health centres and district hospitals, and between the district and central hospitals, could help overcome the transportation barriers to health care.

Enhancing Pharmacovigilance in Sub-Saharan Africa Through Training and Mentoring: A GSK Pilot Initiative in Malawi

“…For cases that cannot be handled in these healthcare centres, secondary-level care is provided by district hospitals, located in each of Malawi's 29 districts, which provide some surgical services. The tertiary tier consists of central hospitals, such as the Queen Elizabeth Central Hospital (QECH) in Blantyre, which have more advanced equipment and specialised medical personnel, and are located in the four main urban areas of Malawi [19].…”

Deaths from surgical conditions in malawi A randomised cross-sectional nationwide household survey

“…The method used in this study is very similar to two other publications which had the same data collectors, a broad questionnaire with different domains and a pool of data that was collected at the same time. Analysis of relevant specific data was extracted based on the required specific question and aims of the different study topics from this data base [11,12].…”

“…This study was a cross-sectional, nation-wide survey, with systematic sampling of participants at the household level. This method was chosen since there are no public records or registers with information about diseases, and surgical condition related deaths at a national level in the Malawi population [11,12].…”

“…The sample size of the households needed for this prevalence study was estimated to be 1487 persons, based on a pilot study which was carried out in rural areas of the capital city, Lilongwe, in 2016 [11,12]. The sample size of individuals was estimated to be 2994 (95% CI) with a design effect of 1.5 at 25 % prevalence of unmet surgical needs in reference to the prior LMIC region reports [11,12]. We planned to perform the sampling in all 28 districts.…”

“…Fifty-five settlements were randomly selected using a computer generated randomisation, choosing settlements from each district. Two or four households were systematically selected in each settlement depending on its size [11,12]. Two households were selected in a settlement with less than 10 households, whereas four households were selected in a larger settlement.…”