[ad_1]
Data source
This study was based on secondary data taken from a nationally representative and unique survey called the Bangladesh Household Income and Expenditure Study (HIES 2016). The Bangladesh Bureau of Statistics (BBS), conducted this survey from April 2016 to March 2017. The final report on this 16th round of HIES described the survey objectives, survey design, sampling technique, survey tools, measuring system, sample size calculation, quality control, and the questionnaire’s new modules [32]. HIES2016 included 46,080 households, selected from 2304 primary sample units (PSUs). The HIES2016 was conducted in 20 strata that fall under three basic localities (rural/urban and city corporation), 8 rural, 8 urban, as well as 4 statistical metropolitan areas (Dhaka Chattogram Rajshahi, Rajshahi, Rajshahi, and Khulna). It provides information on the household, disability (education, health), housing, and a wide variety of socio-economic variables (e.g., family earnings and consumption, assets and housing conditions, as also data on demographic variables, education and employment) that plays a major role in the government’s decision-making process.
HIES 2016 used a stratified cluster sampling design with two stages. At the first stage, PPS (probability proportional to size) systematic sampling technique was used to draw a total of 36 PSU’s from each district, where the number of households in each PSU being the measure of size. The sample design used Enumeration Area as the PSU. This is a group of 110 households according to Population Census 2011. After selection of the PSU’s, a complete household listing in these selected PSU’s was done in the field. Thus, the total calculated sample size for the survey stands at 46,080 (2304 × 20) households. A total of 46.076 households were surveyed. There were 32,096 rural and 13,980 urban areas. There were 186,076 households that were interviewed. The rural area had 130,435 and urban areas had 55,641. Our analysis included both individual-level and household-level data.
Study data
To calculate the wealth quintile based on household characteristics, we first converted each household characteristic into a binary variable. We used the binary variables as a basis for computing the wealth quintile. We used a multivariate technique principal components (PC) analysis to compute the wealth quintile. In this case, the first PC score was used to determine the wealth quintile. These quintiles were then assigned to all individuals based off households.
We excluded certain observations from the source dataset to complete our study population. This was based upon our inclusion/exclusion criteria. (Fig. 2). Among 46,076 households, eight households were primarily excluded due to the lack of respondent’s information. The remaining 46,086 households had 186,083 people. 152,347 of them were not affected by chronic illness. 968 individuals were again excluded because of missing information on any of our socio-economic and demographic factors. To date, we have 32,768 chronically sick individuals that were included in our Study Population. After excluding 26,164 people with missing OOPE (among which, 3393 did NOT report OOPE and 22,771 did no seek medical treatment), data from 6572 chronically sick individuals was used to analyze out-of-pocket expenses. Data from 32 individuals with zero (0 OOPE were also excluded from the 32,768 study population. To avoid any complexity in normalization, we excluded zero values of OOPE (e.g. the natural logarithm for zero is not defined). In the following Fig., you can see the entire process of inclusion/exclusion. 2.
Diagram of the observation inclusion/exclusion process
Variables to study
Outcome variables
The World Health Organization, (WHO), created an International Classification of Functioning, Disability, and Health to help conceptualize disability.https://www.who.int/classifications/icf/en/). The ICF-based method for disabilities requires the development of new measurement models that can be used to conduct surveys and censuses. The Washington Group has adopted six useful questions about disability that they have developed for use in national censuses and surveys. [33]. To be consistent with ICFDH, the HIES2016 used these six disability-related questions. [33]They are having difficulty (i) seeing even if they wear glasses, (ii), hearing even if they have a hearing aid, and (iii). In self-care, such as dressing and washing, or feeding themselves, and (vi). communicating. Each household member was asked to give information about any disability and the severity. Each question had four response levels: (1) No Difficulty, (2) Yes, Some Difficulty, (3) Yes, Severe Difficulty or (4) Yes, Can’t see/hear/walk/remember/self-care/communicate at all. For the convenience of analysis, we converted the presence of disability into two groups: ‘0’ denoted for “No difficulty” and ‘1’ for “Any level of difficulty” [33,34,35]. Moreover, an outcome variable was defined as ‘chronic illness with a disability’ based on disability status and chronic illness (chronic fever, injuries/disability, chronic heart disease, respiratory disease/asthma/bronchitis, diarrhea/dysentery, gastric or ulcer, blood pressure, arthritis/rheumatism, skin problem, diabetes, cancer, kidney diseases, liver diseases, mental health, paralysis, ear/ENT problem, eye problem, and others) with two values: 1 denoting “Presence of at least one of the above six disabilities with chronic illness” and 0 denoting “Absence of disability with chronic illness”. OOPE was determined by adding up direct medical expenses, such as hospital outpatient fees, physician fees and diagnostic fees. Also, any other non-medical supplies and direct costs such as transportation and lodging, tips and other costs. [32]. BBS did not include indirect costs like lost opportunity, productivity and other intangible cost, such as pain or suffering, in the HIES2016. We were therefore limited to only include direct healthcare costs in the calculation for OOPE. In Bangladesh, OOPE is measured in Bangladeshi Taka (BDT) equivalent to the U.S. $0.01163 (i.e., 1 USD = 86 BDT).
Independent variables
To take into account the minority problem and the coastal climate crisis, we defined and categorized ‘Religion’ as ‘Muslim/Non-Muslim’ and ‘Region’ as ‘Exposed Coast/Non-Exposed’. The ‘Non-Exposed’ region consists of interior coast and non-coastal areas. In Bangladesh, Muslims make up the majority while non-Muslims (Hindus Buddhis, Christians, and others) are led by Hindus. Uddin and Kaudstaal provide a detailed description of the exposed and non-exposed coasts. [36], and Bahauddin (et al. [37]. The coastal regions of Bangladesh, with 19 districts containing 147 Upazilas, cover/occupy 32% of the country’s total geographic area, wherein 28% of the country’s total population live. To be more precise, we also identified a division between the coast areas as the exposed coastline and the interior coast. These areas, which contain 48 Upazilas, are exposed to the ocean and/or lower estuaries. Later areas, with 99 Upazilas, were added to our non-exposed category. The non-exposed area includes coastal interior and other non-coastal areas. In Fig. 3QGIS (version 2.8.5 Wien) software is freely availablehttps://qgis.org/en/site/Or https://qgis.org/downloads/).
Coastal areas of Bangladesh This figure was created by QGIS (version 2.8.5 Wien) software.https://qgis.org/en/site/Or https://qgis.org/downloads/)
The variables used to predict the burdens of chronic illness and OOP healthcare spending were: religion, region, age, sex, marital status, wealth quintile (being classified as public, private and pharmacy/dispensary), wealth quintile (being categorized into poorest: lowest 20% and middle: 3rd quintile), and employment/earnings status (Yes/No). Respondent’s age was categorized as childhood (≤ 19 years), young adulthood (20–39 years), middle-aged (40–64 years), senior-aged (65–84 years), and old senior-aged (≥ 85 years) [38]. We divided marital status into three categories: married, unmarried, separated, and others (widowed or divorced). Education level was classified as primary, secondary, higher secondary, primary, and higher education. One variable was added to the list of predictors for OOPE that described whether respondents were currently enrolled in any SSNP or had received any assistance.
Statistic analysis
We summarized data for each variable using descriptive statistics. We also tabulated chronic disease by disability status in frequency count as well as percentage. Before beginning the analysis of OOPE, we checked the Kolmogorov–Smirnov test to ensure that the OOPE was normal. Since the distribution of OOPE was non-normal (right-skewed), we summarized OOPE by the median and interquartile range (IQR), and the Wilcoxon-Mann-Whitney test was used to compare OOPE between two groups. The distribution of right-skewed OOPE data was normalized by applying a natural logarithmic transform to OOPE. Bi-directionally connected two burdens – health and financial – were linked in box plots. Multiple logistic regression analysis was used to examine the relationship between disability status and religion and region, and to calculate odds ratios (ORs), for determining the odds that a minority group or people from the exposed coast will develop disability. Multiple linear regression was used to evaluate the effect of explanatory factors on OOPE. Data processing (Data cleaning, derivation and creation of analysis datasets), validation, and all statistical analyses were done using SAS 9.4 software (SAS Institute, Inc., Cary, North Carolina, USA).
