Relationship between Adequate Healthcare Facilities and Population Distribution in Melaka Using Spatial Statistics

Healthcare facilities are required for all levels of a population regardless of age, race or socioeconomic status. Provision for healthcare facilities requires knowledge of population data and area for placement of healthcare facilities. The main objective of this paper is to examine the relationship between availabilities of healthcare facilities and population in districts in Melaka. This study is based on population data and distribution of healthcare facilities which were obtained from the Department of Statistics Malaysia and Malaysia Administrative Modernisation and Management Planning Unit (MAMPU). Both types of data were converted into geographic information systems (GIS) data format using QuantumGIS. Then these data were analysed using two main methods using GeoDa and ArcGIS applications. First is by using a formula set by a global standard, ISO37120 to measure healthcare facilities adequacy. Secondly, spatial statistics, Bivariate Moran’s I was used to examine the relationship between population and healthcare facilities distribution. Local Moran’s I was used to examine the cluster of population distribution. Findings show the allocation of healthcare facilities is sufficient according to the Malaysian Community Facilities Guideline. A high-high cluster of the population is found in Melaka Tengah District. However, the relationship between the total population and number of public hospital in-patient beds are negatively correlated. Similar results are obtained for private clinics and pharmacy. This result shows higher population distribution has less number of public hospital inpatient beds, the number of private clinics and pharmacy. However, Bivariate Moran’s I analysis yields a different output for the public clinic. This result shows the high number of population distribution is positively correlated with a high number of public clinics. Thus, authorities, in this case, would be the Ministry of Health and Melaka state government should be aware of the current availability of healthcare facilities to its population in ensuring a high level of healthcare services provided in the state.


INTRODUCTION
Healthcare facilities are required for all levels of population. Adequate healthcare facilities contribute to better population health (Gulliford, Jack, Adams, & Ukoumunne, 2004). Availability of healthcare facilities is often being debated. Total number of beds and doctors at medical institutions for example, are considered as essential parameters in providing facilities to the general public (Chakraborty & Sen, 2011). Most developing countries face difficulties in providing these facilities. Nigeria, for example, faced issues of better equity in accessing healthcare facilities and quality of services (Oyekale, 2017). Although the number of healthcare facilities may be equally distributed among the local government areas, it may not yield the true indicator. Sometimes the location of health facilities plays a big role in creating higher accessibility to the local community. Difficulties in accessing the facilities by the rural communities, rapid population growth and lack of proper attention to patients may not reflect adequate supply of healthcare facilities (Ujoh & Kwaghsende, 2014). Countries in Europe are creating larger and more specialised healthcare facilities to achieve economies of scale. However, findings by Pantzartzis, Edum-Fotwe and Price (2017) show small facilities can be more viable. Thus, fitting mitigation, adaptation strategies and resilience practices in meeting specific needs would be the best approaches to be considered (Ilesanmi & Mgbemena, 2015).
This study intends to examine the relationship between availabilities of healthcare facilities and population in districts in Melaka. In this study, healthcare facilities refer to hospitals, clinics and pharmacies.

ADEQUATE OR ACCESSIBLE HEALTHCARE FACILITIES
Many methods have been used to determine the allocation of healthcare facilities. Among others include, integrated accessibility and locationallocation models (Polo, Acosta, Ferreira, & Dias, 2015), framework for emergency and non-emergency healthcare facilities (Ahmadi-Javid, Seyedi, & S.Syam, 2017), probability metric to social programs (Radke & Mu, 2000), a two-step floating catchment area method based on geographic information system (GIS) (Kanuganti, Sarkar, & Singh, 2016;Song, Zhu, Mao, Li, & An, 2013), three-step floating catchment area (Wan, Zou, & Sternberg, 2012), multi-modal two step floating catchment area analysis (Langford, Higgs, & Fry, 2016) and a cumulative case ratio (Zinszer et al., 2014). Some scholars review the methods used. Rahman and Smith (2000) review the use of location-allocation model. Daskin and Dean (2004) on the other hand, review the location set covering model, maximal covering model and P-median model. Most of these researches focus on accessibility. Thus, proximity to healthcare facilities and demand for healthcare services seem to be the main criteria in selecting the best location.

STANDARD AND GUIDELINE OF HEALTHCARE FACILITIES
According to ISO37120 (2014), the number of in patient public hospital beds (core indicator) can be used as one of the indicators to monitor the level of health service delivery. This score is calculated by dividing the number of inpatient hospital beds and the total number of population which is later multiplied by 100000 (ISO, 2014). Different countries have different guidelines in providing healthcare facilities. In Malaysia, healthcare facilities provision is guided by a special guideline known as Community Facilities Guideline (Jabatan Perancangan Bandar dan Desa Semenanjung Malaysia, 2013). It is designed to assist state authority, local authority, implementing agencies and developers in providing adequate community facilities towards creating a liveable community. Table 1 shows the number of population and the number of in-patient beds in hospitals. In this guideline, the planning provision for clinics is also assigned. The main component of this clinic includes services for maternity, outpatient, dental and pharmacy. Table 2 shows the number of population per clinic. Both standards and guidelines are used to measure the allocation of healthcare facilities provided in the study area. The standard is used to measure the ratio in accordance with the global indicator, and the guideline is used to measure the allocation provided is adequate according to the population.

METHODOLOGY
This study employs a quantitative approach using secondary data on population and healthcare facilities. Melaka is chosen as the study area due to the availability of data on healthcare facilities by districts and population data by mukims, the smallest administrative unit available in Malaysia. Both data are not available for other states in Malaysia. Furthermore, Melaka is one of the flood resilient states.

The Study Area
Malacca is in the central region in Malaysia. The area of Malacca is 165,480.94 hectares. Malacca is divided into three main areas, namely Jasin, Alor Gajah and Malacca Tengah and has 58 mukims. Mukim is the smallest administrative unit in any state in Malaysia.
The main land use of Malacca is agriculture with an area of approximately 118,488.35 hectares (71.6%), followed by housing and settlement of 10,019.85 hectares (6.05%), industrial, institutions and businesses (22.35%). Malacca has a flat topography drained by rivers flowing from the northeast to the southwest of the Strait of Malacca. However, there are some high-lying areas around the northern states such as Bukit Manis (169m), Hill End (210m), Bukit Punggur (397m), and Bukit Batang Malacca (433M). Only about 0.10% of land is within the category of slope over 25 degrees which is not suitable for development. This statement shows that Malacca has a good potential for development and lacks physical barriers that might hinder development. The population in the state of Malacca is composed of a majority of the Malay community (56.48%), Chinese (32.76%), Indian (4.19%), others (2%), and non-Malaysian (4.57%) (Department of Statistics, 2017).
Population data based on age and districts were obtained from the Department of Statistics Malaysia eStatistics portal (Department of Statistics Malaysia, 2017). Melaka Tengah has the highest percentage of the population as compared to the other two districts, Alor Gajah and Jasin. Table 3 shows the details of the population in Melaka according to districts.  Table 4 shows the number of health facilities according to types at the district level. The types of health facilities available in Melaka include hospital, community polyclinic, 1Malaysia clinic, rural clinic, private clinic, dental clinic, private pharmacy and health office.  Table 5 shows the number of beds according to hospitals and districts.

Geographic Information Systems Database
Before analysis, a geographic information systems (GIS) database was developed on Melaka to achieve the objective of this study. The various software was used to produce this database. Quantum GIS is the software used to develop the GIS database and GeoDa is used to analyse the data spatially. Both software is open source software that is available for researchers to utilise.

Analysis
Tobler's first law of geography states "everything is related to everything else, but near things are more related than distant things." (Tobler, 1970). Thus, spatial statistics will be able to explain the significant level of spatial distribution of any feature on earth.
There are various tools that can be used to examine spatial distribution. Among others include nearest neighbourhood analysis and Moran's I. Nearest neighbourhood analysis can determine the distribution of health facilities whether they are clustered, dispersed or randomly distributed (Lee and Wong, 2001). However, the value of each feature in relation to its distance with another feature is unknown of its significant level. To achieve this Moran's I is used. Moran's I (Global Moran's I) refers to a measure of spatial autocorrelation that considers both location and values a feature carried simultaneously. However, Moran's I can only measure a single variable at any one time, and this can only be achieved by using Bivariate Moran's I. Bivariate Moran's I allows researchers to determine the relationship between the concentration of population and the placement of health care facilities. This tool will determine whether the provision of health care facilities is by the allocation stated in the guideline.

FINDINGS
Population in Melaka is highly concentrated in Melaka Tengah. Local Moran's I output gives a high-high cluster of the population in this district. The other two districts, Alor Gajah and Jasin, show no significant pattern. Figure 1 shows the output of Local Moran's I analysis of population distribution in Melaka.

Figure 1 Output of Local Moran's I analysis of population distribution
Melaka has a high number of private hospital beds due to health tourism promoted by the state government. However, in this study, only public hospital beds are considered. Calculation of ISO37120 standard shows a ratio of a number of hospital beds according to districts. In this calculation, Melaka Tengah has the highest ratio of public hospital beds. Figure 2 shows the distribution of the said ratio.  Table 6 shows the details of Moran's I score using Bivariate Moran's I analysis. Similarly, the number of private pharmacies must also be increased based on population distribution at mukim level. Placement of these pharmacies must meet the demand of the population for any specific mukims. Private clinics, however, are not in demand by the population. This might be due to the high number of public clinics which are accessible at mukim's level. Many studies concentrated on providing a method of determining the best location of healthcare facilities and accessibility. However, this study provides a slightly different research focus in examining the allocation and location of healthcare facilities based on the spatial analysis.

CONCLUSION
Examining provision of healthcare facilities should be carried out using multiple methods of measurement. Guideline alone may not be able to handle rapid population growth, accessibility, and quality of services. Location of healthcare facilities plays a major role in meeting the demand of the population at mukim's level. The population of any state is not equally distributed. Therefore, placement of healthcare facilities must consider the distribution of the mukim's population before implementation. Bivariate Moran's I has enabled to prove the inconsistencies of healthcare facilities placement. Thus, policy makers and decision makers need to consider multiple methods of analysis to assist them in making wise decisions.

ACKNOWLEDGEMENT
Authors wish to thank the University of Malaya for grant no. BKP011/13 and Ms Wan Suzita Wan Ibrahim.