Background: Solid medical waste (SMW) is generated from the healthcare industry but can also be found in households when activity involving patient care occurs. Its hazardous properties require special treatment to minimize hazards to the environment. To achieve this, SMW must be safely diverted from house-holds using a systemic approach, which should be informed by the quantities generated and factors associated with generation. Objective: To characterize household SMW in terms of quantity and composition and to describe the factors associated with its generation. Methods: Manual sorting of household waste was conducted in 60 households to measure quantities of SMW and its components in Ga South Municipal Assembly, Accra, Ghana. Sample collection took place in the wet season (October, 2014) and dry season (December, 2014/January 2015). Rates of generation and percentage composition computed. Factors influencing generation were evaluated with non-parametric tests and quantile regression analysis. Statistical significance was set at p < 0.05. Results: Per capita generation of SMW was 1.77 × 10 -3 kg/person/day. Pharmaceutical waste and sharps waste comprised 98% and 2% of SMW respectively. Generation rates were significantly higher in the wet season than in the dry season (z = 3.129, p = 0.002). Households where medical complaints were reported generated significantly less SMW at the 5th, 10th, 25th and 50th quantiles (β = -2.711, p = 0.001; β = -2.949; p < 0.001; β = -3.429, p < 0.001; β = -4.600, p < 0.001 respectively). Conclusion: SMW was generated in relatively small quantities in households. However, the large proportion of pharmaceuticals with mostly antibiotics raises concerns about drug resistance among other potential hazards.
The health care industry generates waste containing pathogens, toxic heavy metals and chemicals, termed healthcare waste (HCW) [
Sharps in the waste stream such as hypodermic needles can cause physical injury and may lead to transmission of blood borne pathogens if present. Expired and unused medicines discarded with household waste, often end up in landfills, where household waste is mostly landfilled. Active pharmaceutical ingredients from discarded medicines in landfills can be discharged into leachate [
In recognition of these health hazards, some safety measures have been applied elsewhere. For instance, collection of sharps waste is available for households in some parts of the United States [
Ga South Municipal Assembly (GSMA) was created in 2009 in south western Accra. It has a total population of 411,377, of which 48.9% are males (unpublished document, Ghana Statistical Service, 2014). It is predominantly urban, comprising of towns with populations ranging between 5000 and 20,000. Estate development and commerce have outpaced the earlier agro-based economy (unpublished document, Municipal Planning and Coordinating Unit, 2014). Unpublished reports from the Municipal Assembly Waste Management Department, estimate total solid waste generation at 19.43 tonnes daily, which is managed by public-private partnership.
A household was the sampling unit in consistency with earlier studies [
Identification numbers were assigned to participating households comprising four digits. The first two digits represented their location (numbered 01 to 30) and the second two digits were their serial numbers from the sampling frame (numbered 01 to 20 in each location). Black household bin bags of dimension, 725 × 975 mm, and 80-litre plastic household bins were labelled with the assigned numbers. Each selected household was informed about the collection schedule (6 am to 8 am), given one bin and two bin bags, and asked to store their waste as routinely done. They were advised to keep the bins covered to prevent stray animals from tampering with the waste. A pilot study conducted a week earlier in nine households indicated that daily collection of household waste did not yield meaningful quantities of SMW. Therefore waste collection was conducted weekly in 2 phases that lasted a total of five weeks. The first phase lasted two weeks in October, 2014 (wet season); and the second phase lasted three weeks from December, 2014 to January, 2015 (dry season). New bin bags were provided during waste collection for the following week’s collection. Retrieved bin bags with content were transported to a location appointed for manual sorting.
The unsorted content of each household’s bagged waste was weighed before sorting. To obtain the quantity of specific waste components, manual sorting was undertaken by four trained field staff on a table overlaid with a wire mesh on a clean plastic sheet. The specific waste components were pharmaceutical waste, sharps waste and offensive waste (
The waste components were manually sorted and each fraction was weighed. Measurements were estimated on the basis of wet waste (w/w) in kilograms (kg) using a scale GBK 120 with a precision of 0.005 kg (Adam Equipment Company, 2013) for total household solid waste (HSW) and an additional scale with a capacity of 5 kg and a precision of 0.001 kg for household SMW. At the onset of the survey both scales were calibrated against standard weights of 5 Newtons (0.5099 kilograms) and then standardized between measurements. Electronic records were created to store weekly records of waste measurements. Parameters were analysed using the Statistical Package for Social Sciences (SPSS) version 22.
A waste survey was conducted by four trained data collectors using a purpose designed 32-item questionnaire to obtain respondent and household characteristics. Sociodemographic characteristics were household income, respondent’s educational status and room occupancy. Characteristics related to health were: medical complaints, children aged below 5 years in household and National Health Insurance Scheme (NHIS) membership status. The questionnaire was pre-tested and administered in each household, mostly in the evenings. An eligible
| Waste Sub-streams | Description | Examples |
|---|---|---|
| Pharmaceutical waste | Expired, unwanted or left over medicines which were discarded. It also includes containers contaminated with residue/contents of pharmaceuticals and drug vials. | Syrup bottles, blister packs with residue/content, drug vials, loose tablets |
| Sharps waste | Items that cause cuts or puncture wounds which have been discarded. | Needles, syringes with needles attached, broken glass vials, blades, shaving sticks |
| Offensive wastea | Discarded items which have come in contact with body fluid, although not known to be infectious but causes offense to those who come into contact with it. | Plaster, soiled tissue, condoms, sanitary pads, diapers |
a. Solid medical waste comprised pharmaceutical waste and sharps waste; offensive waste was analysed separately.
respondent was an adult aged 18 years or older who was either well-informed about housekeeping arrangements or resident for at least one month in the household. If there was more than one eligible respondent, selection was done by ballot.
Descriptive statistics of the sample households were generated. Other variables computed were household daily waste generation rate, per capita daily generation rate, percentage waste weight (%). Monthly household income was arbitrarily assigned categories based on the range of incomes obtained from the sample (GHC 20 - GHC 900; $76 - $236.85). There were three categories: low (≤GHC 200; $52.63), middle (GHC 201 - GHC 300; $52.89 - $78.95) and high income groups (>GHC 300; >$78.95), somewhat similar to [
The data were analyzed using non-parametric tests. The Kruskal-Wallis H test evaluated variation in daily generated quantities of SMW across assigned income groups. Seasonal variation was assumed a priori in household generation of SMW and evaluated with the Wilcoxon signed ranks test. Household characteristics that might influence generation of SMW were determined in two steps. First, within-variable differences in household SMW generation were tested using the Wilcoxon rank sum test. Each characteristic namely medical complaints, National Health Insurance Scheme (NHIS) membership, education, presence of under-fives, type of house and room occupancy, had “1” assigned to the risk category and ‘0’ to the reference category. A p-value < 0.05 indicated significant variation in household generation of SMW. In the second step, quantile regression was used for multivariable analysis, applying the statistical model in Equation (1) [
Y = α + ∑ i β i X i + ε (1)
where Y is the household generation of SMW (kg/household/day), α is a constant term, βi represents the regression coefficient for ith household characteristic, Xi, and the residual error term is represented by ε [
Sixty households were recruited for the household waste stream analysis. In this sample, 42 (70.0%) households were registered under the National Health Insurance Scheme (NHIS), 17 (28.3%) households reported medical complaints, 23 (38.3%) households had children aged below 5 years and in 48 (80.0%) households the respondents had attained secondary education or higher. Fifty (83.3%) households were single (nuclear) families, 27 (45.0%) households lived in compound houses (multi-unit housing), and 40 (66.7%) households had 4 sleeping rooms or less. The average household size in the low, middle and high income groups was 4 persons, 5 persons and 4 persons respectively.
The average quantity of SMW generated in households was 7.26 × 10−3 kg/ household/day and each household member generated 1.77 × 10−3 kg/person/ day on average (
Two households were outliers with generation rates of 54.85 × 10−3 kg/ household/day (household number = 18/20) and 74.48 × 10−3 kg/household/day (household number = 09/02) respectively. Both households discarded an unusual quantity of medicines in their household waste for unknown reasons. When these households were excluded, average generation was 5.28 × 10−3 kg/household/day and each household member generated 1.34 × 10−3 kg/ person/day respectively (
Based on capita generation and population size in the 30 locations (n = 24,183), the average daily quantity of SMW generated was 42.80 kg when outlier measurements were included and 32.40 kg, when they were excluded. No significant variation was found in SMW generated across the income groups per household [H(2) = 1.40, p = 0.497] and per capita [H(2) = 3.08, p = 0.214].
During the study period, the average percentage composition of SMW in
| Name of parameters | Household generation (kg/hh/day) | Per capita generation (kg/person/day) |
|---|---|---|
| Mean (n = 60 households) | 7.26 × 10−3 | 1.77 × 10−3 |
| Standard deviation | 11.58 × 10−3 | 2.64 × 10−3 |
| Coefficient of variation | 1.60 | 1.49 |
| Median | 4.59 × 10−3 | 1.05 × 10−3 |
| Interquartile range | 4.85 × 10−3 | 1.18 × 10−3 |
| Minimum (household number = 01/05) | 28.6 × 10−6 | 8.16 × 10−6 |
| Maximum (household number = 09/02) | 0.74 × 10−3 | 0.15 × 10−3 |
| Coefficient of skewness | 4.51 | 3.72 |
| Coefficient of kurtosis | 24.52 | 17.87 |
| Name of parameters | Household generation (kg/hh/day) | Per capita generation (kg/person/day) |
|---|---|---|
| Mean (n = 58 households) | 5.28 × 10−3 | 1.34 × 10−3 |
| Standard deviation | 3.98 × 10−3 | 1.23 × 10−3 |
| Coefficient of variation | 0.75 | 0.92 |
| Median | 4.38 × 10−3 | 1.01 × 10−3 |
| Interquartile range | 4.71 × 10−3 | 1.05 × 10−3 |
| Minimum (household number = 01/05) | 28.6 × 10−6 | 8.16 × 10−6 |
| Maximum (household number = 02/03) | 1.72 × 10−2 | 0.57 × 10−2 |
| Coefficient of skewness | 1.18 | 1.60 |
| Coefficient of kurtosis | 4.10 | 5.41 |
a. Two households were excluded from the original sample in
household waste was 1.07% (offensive waste excluded). Pharmaceutical waste formed the bulk (approximately 98%) of SMW from households. The percentage distribution of sharps waste was similar across income groups. When all income groups were combined, the percentage of pharmaceutical waste, sharps waste and offensive waste in household waste were 1.05%, 0.02% and 4.94% respectively.
When all income groups were combined, the per capita daily generation rates were significantly higher in the wet season than in the dry season for pharmaceutical waste, solid medical waste and offensive waste. Sharps waste showed no seasonal variation (z = 1.938, p = 0.053) (
Preliminary analysis showed that medical complaints, type of house, and room occupancy might influence the distribution of SMW (
After multivariable analysis, reported medical complaints emerged as the only significant factor influencing generation of SMW. Households that reported medical complaints generated significantly less SMW than households that did not report medical complaints, controlling for type of house and room occupancy (
Among items recovered from pharmaceutical waste, antibiotics, multivitamins,
| SMW components\ Income group | Low income | Middle income | High income | Total | Wilcoxon signed rank test | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Wet season | Dry season | Wet season | Dry season | Wet season | Dry season | Wet season | Dry season | Z | p-valueb | |
| Pharmaceutical waste | 4.31 × 10−3 | 1.10 × 10−3 | 0.81 × 10−3 | 1.06 × 10−3 | 1.97 × 10−3 | 0.98 × 10−3 | 2.79 × 10−3 | 1.06 × 10−3 | 3.052 | 0.002 |
| Sharps waste | 0.06 × 10−3 | 0.04 × 10−3 | 0.05 × 10−3 | 0.03 × 10−3 | 0.04 × 10−3 | 0.03 × 10−3 | 0.05 × 10−3 | 0.04 × 10−3 | 1.938 | 0.053 |
| Solid medical waste | 4.36 × 10−3 | 1.15 × 10−3 | 0.87 × 10−3 | 1.07 × 10−3 | 2.01 × 10−3 | 1.01 × 10−3 | 2.84 × 10−3 | 1.09 × 10−3 | 3.129 | 0.002 |
| Offensive waste | 34.89 × 10−3 | 10.65 × 10−3 | 38.86 × 10−3 | 10.28 × 10−3 | 14.28 × 10−3 | 2.90 × 10−3 | 30.97 × 10−3 | 8.61 × 10−3 | 4.960 | 0.000 |
aValues under wet and dry season are in kg/person/day. bp-values in bold font are statistically significant at 5% significance level.
| Characteristics | Median quantity of solid medical waste (IQR)a | Mann-Whitney test statistic | p-valueb |
|---|---|---|---|
| NHIS | |||
| No | 3.80 × 10−3 (4.71 × 10−3) | −0.008 | 0.994 |
| Yes | 4.80 × 10−3 (5.08 × 10−3) | ||
| Medical Complaints | |||
| No | 5.86 × 10−3 (4.80 × 10−3) | 3.986 | 0.000 |
| Yes | 2.31 × 10−3 (1.89 × 10−3) | ||
| Highest level of education | |||
| None/Basic | 3.81 × 10−3 (4.33 × 10−3) | 0.213 | 0.832 |
| Secondary or higher | 4.80 × 10−3 (5.09 × 10−3) | ||
| Children aged below 5 years | |||
| Yes | 3.89 × 10−3 (3.17 × 10−3) | 0.395 | 0.693 |
| No | 4.71 × 10−3 (5.65 × 10−3) | ||
| Type of House | |||
| Compound house | 3.51 × 10−3 (3.12 × 10−3 ) | 2.244 | 0.025 |
| Flat/Other | 5.94 × 10−3 (6.88 × 10−3 ) | ||
| Room occupancy (category) | |||
| ≤2 person(s) per room | 5.28 × 10−3 (5.02 × 10−3) | 2.038 | 0.042 |
| >2 persons per room | 2.66 × 10−3 (1.72 × 10−3) | ||
| Family type | |||
| Single family | 4.67 × 10−3 (4.71 × 10−3) | 0.615 | 0.539 |
| Extended family | 3.73 × 10−3 (5.29 × 10−3) |
a. IQR is the interquartile range. b. p-values in bold font are statistically significant at 5% level of significance.
analgesics, antifungal and antimalarial drugs were present, with antibiotics being predominant. For instance, syrup bottles containing amoxicillin and metronidazole (mostly as paediatric formulation), capsules of ampicillin, cloxacillin, doxycycline, and penicillin V tablets were found. Non-steroidal anti-inflamma- tory drugs (NSAIDs) included diclofenac and ibuprofen. Paracetamol was found
| Variable | OLS | q05 | q10 | q25 | q50 | q75 | q90 | q95 |
|---|---|---|---|---|---|---|---|---|
| constant | 9.086 | 2.720 | 3.149 | 4.286 | 7.149 | 8.486 | 13.371 | 15.829 |
| p-value | 0.000 | 0.013 | 0.000 | 0.000 | 0.000 | 0.000 | 0.220 | 0.417 |
| Medical complaints | −5.935 | −2.711 | −2.949 | −3.429 | −4.600 | −2.943 | −4.914 | −10.200 |
| p-value | 0.076 | 0.001 | 0.000 | 0.000 | 0.000 | 0.177 | 0.668 | 0.610 |
| Type of house | 1.013 | 0.019 | −0.171 | −0.771 | −1.292 | −0.169 | 3.857 | 9.143 |
| p-value | 0.739 | 0.984 | 0.780 | 0.284 | 0.270 | 0.945 | 0.796 | 0.737 |
| Room occupancy (category) | −5.152 | −1.168 | −1.406 | −1.594 | −3.429 | −2.711 | −9.651 | −12.108 |
| p-value | 0.276 | 0.176 | 0.012 | 0.141 | 0.056 | 0.312 | 0.365 | 0.406 |
| R2 c | 0.0756 | 0.1581 | 0.1645 | 0.1208 | 0.1010 | 0.0752 | 0.0769 | 0.0890 |
a. All coefficient values for the study attributes are multiplied by (×10−3). b. The probability values (p-value) support the hypothesis that the computed coefficient equals zero. A p-value of 0.05 or less indicates a statistically significant effect at 5% significance level. c. The coefficient of determination (R2) in quantile regression models are Pseudo R2.
| Variable | OLS | q05 | q10 | q25 | q50 | q75 | q90 | q95 |
|---|---|---|---|---|---|---|---|---|
| constant | 6.343 | 0.857 | 2.720 | 3.488 | 6.628 | 7.971 | 11.568 | 13.371 |
| p-value | 0.000 | 0.391 | 0.005 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Medical complaints | −3.236 | −0.771 | −2.006 | −2.631 | −4.318 | −5.107 | −5.940 | −2.457 |
| p-value | 0.005 | 0.406 | 0.030 | 0.001 | 0.001 | 0.005 | 0.154 | 0.666 |
| Type of house | −0.801 | −0.086 | −0.686 | −0.749 | −1.343 | −1.823 | −2.801 | 3.114 |
| p-value | 0.465 | 0.914 | 0.318 | 0.322 | 0.266 | 0.281 | 0.380 | 0.480 |
| Room occupancy (category) | 0.525 | 1.229 | −0.029 | 0.403 | 0.320 | 1.621 | 2.546 | 0.743 |
| p-value | 0.634 | 0.214 | 0.973 | 0.614 | 0.776 | 0.342 | 0.488 | 0.868 |
| R2c | 0.1523 | 0.1824 | 0.1817 | 0.1559 | 0.1306 | 0.1106 | 0.1035 | 0.0695 |
a. All coefficient values for the study attributes are multiplied by (×10−3). b. The probability values (p-value) support the hypothesis that the computed coefficient equals zero. A p-value of 0.05 or less indicates a statistically significant effect at 5% significance level. c. The coefficient of determination (R2) in quantile regression models are Pseudo R2.
in dispensing envelopes, blister packs and as loose tablets. Antihypertensive (nifedipine, lisinopril, amlodipine, bendrofluazide) and antidiabetic (glibenclamide, metformin) medicines were also present in blister packs or dispensing envelopes. Sharps recovered were predominantly used razor blades. A few disposable shaving sticks were also present. Needles (capped and uncapped) and syringes were recovered from a single household bin bag. Offensive waste mostly comprised of soiled baby diapers, with a smaller fraction of blood stained tissue paper, sanitary pads and cotton buds. Other items found in single disposal events included plaster, a long wrap of gauze bandage soiled with serous fluid and a pair of examination gloves. Male condoms and intravenous infusion bags (normal saline) were found on two disposal events. No intravenous tubing or cannulae were seen.
At the time of writing, no study had described the quantification of SMW in household solid waste in Ghana, therefore our study represents the first generation study conducted in a local community. Households typically generated between 5 and 7 grams of SMW daily. Although these were relatively small amounts, the total daily production of 32.40 kg computed for the study population is substantial. With the outlier households, the daily production of 42.80 kg represents situations where hoarding of medicines may occur in the household. This could result in periodic or one-off disposal of large quantities of SMW. The small sample of households used in the study suggests that the values are indicative, but its composition mostly of unwanted medicines, especially antibiotics raises concern. This is given the fact that nearly all household waste in Ghana is sent to landfills.
Household generation of SMW showed significant seasonal variation. This confirmed our assumption a priori. Some diseases that exhibit seasonal variation often require the use of medicines, such as malaria and respiratory tract infections. This would result in the generation of SMW from left over or expired medicines. Waste generated would include medicines and/or their containers, but not packaging. The higher generation of SMW in the wet season may be partly attributed to common acute illnesses which tend to peak with the rains, such as malaria, respiratory tract infections and some diarrhoeal diseases. The medicines recovered during the waste stream analysis included therapeutic categories often prescribed or bought over the counter for these conditions. Therefore, the consumption of medicines may be higher in these seasons and left over medicines and their containers generate SMW. It is also possible that at the onset of the survey which was in the wet season, a few households may have utilized the opportunity to discard stored waste items since waste collection was offered at no cost to households, whereas the services rendered by the waste management companies had to be paid for monthly. However, these outliers were limited to less than 5% of the sampled households.
Cussiol et al. (2006) sampled municipal solid waste (mostly of residential origin) to quantify potentially infectious waste [
Unwanted medicines comprised the bulk of SMW in households. The therapeutic categories of the medicines recovered were consistent with acute and chronic diseases prevalent in Ghana. Acute conditions such as malaria, respiratory infections and diarrhoea [
Offensive waste is not considered medical waste, but deserves mention because it accounted for a higher overall percentage of household waste (4.94%) compared to SMW (1.07%); and it was often soiled with faecal matter or body fluids. Faecally soiled diapers and used sanitary pads were recovered from the household waste stream analysis, with the former in large quantities. Faecally soiled materials can contain enteric pathogens such as E. coli, Salmonella, and Shigella, which have been reported to survive up to 117 kilometres from source when introduced into flowing water [
This study presents a quantitative and qualitative description of household SMW in a district in Ghana. Only a few studies in Africa have reported on SMW in the community and these often lack quantitative estimates. Empirical data in this study, though indicative, provide baseline data for further generation studies and informs waste management in the district. If SMW should be segregated at source and diverted from the household waste stream, storage and transport capacities can be computed. It draws attention to the large proportion of SMW comprising mostly pharmaceutical waste (mostly antibiotics and non-steroidal anti-inflammatory steroidal drugs) sent to landfills and raises concerns about potential antibiotic resistance and toxicity to wildlife.
At the onset of the study, some of the households did not place their household waste for collection as agreed. This changed and collection improved in the later weeks. To compensate for these events, single mean imputation for missing data was used to compute the missing weight measurements in Stata version 14.0 (StataCorp LP, Lakeway Drive, Texas, USA).
Due to the small sample size of households for the waste stream analysis, results can only be considered indicative. To obtain quantitative estimates intended for regional planning, larger samples taken over successive surveys are recommended. The non-normal distribution of weight measurements of SMW is due to its generation in relatively smaller quantities compared to healthcare facilities. The clustering of measurements close to zero, and fewer extreme values often resulted in a positive skew. The weight of the medicine containers might have affected the weight of SMW, however these were not disregarded as residue left in them can contain active ingredients. Finally, the assignment of income groups arbitrarily, limits the generalization of the results beyond the study location.
Generation of SMW is influenced by medical complaints and is higher in the wet season than in the dry season. As SMW comprised largely of pharmaceuticals, segregation at source could divert this sub-stream for appropriate treatment and disposal to minimize any potential environmental and/or health impact. The relatively smaller quantities of sharps confer some hazardous properties on household waste and should be safely diverted from the waste stream. The impact of continual deposits of SMW generated at computed rates in the study area is unknown, but extant literature and waste composition rationalize concerns about antibiotic resistance and toxicity to wildlife. Therefore, it is pertinent that future policy on the management of SMW takes into account quantities generated in the community, rather than focus on healthcare institutions alone.
Ethical approval for the study was obtained from the Noguchi Memorial Institute of Medical Research (NMIMR) Institutional Research Board. Written permission to conduct the study was obtained from the Municipal Chief Executive (MCE). Clearance was obtained verbally from community chiefs during a meeting convened by GSMA. At the meeting, the purpose of the study and the stages in the study were briefly explained. Individual informed consent was obtained from all persons prior to questionnaire administration. Respondents were interviewed in the privacy of their homes and only eligible households participated in the study. Participants’ rights to withdrawal from the study were upheld and all participants were treated with respect.
All authors declare that they have no competing interests.
The funding for this research came from a PhD fellowship grant awarded to EAU by Building Stronger Universities in Developing Countries―Platform for Human Health of the Danish Government, in collaboration with the University of Ghana. Other than training in basic research methods and proposal development, the funders played no further role in this research.
EAU conceived, designed and analyzed the study. Methodology was reviewed by GG and JNF. Data collection was coordinated by EAU. All authors made significant contributions to the writing of the manuscript. The final manuscript was reviewed and approved by all authors.
The authors gratefully acknowledge the Building Stronger Universities in Developing Countries―Platform for Human Health, DANIDA and the University of Ghana, which provided funding for the larger study of which this study is a part. We are also grateful to the Ga South Municipal Assembly, its Health Directorate, Chiefs, communities and the field staff for their support during the conduct of the study.
Udofia, E.A., Gulis, G. and Fobil, J.N. (2018) Making a Case for Diversion of Solid Medical Waste from Households: A Generation Study in Ga South Municipal Assembly, Accra, Ghana. Journal of Environmental Protection, 9, 50-66. https://doi.org/10.4236/jep.2018.91005
CANSIs: Community Acquired Needle Stick Injuries
DANIDA: Danish International Development Agency
EAs: Enumeration Areas
GSMA: Ga South Municipal Assembly
GSS: Ghana Statistical Services
HCW: Healthcare Waste
MCE: Municipal Chief Executive
NHIS: National Health Insurance Scheme
NMIMR: Noguchi Memorial Institute of Medical Research
OLS: Ordinary Least Squares Regression
SMW: Solid Medical Waste
WHO: World Health Organization